#CUDA memory model

[jcmarchi]

Master CUDA: For Machine Learning Engineers

New Post has been published on https://thedigitalinsider.com/master-cuda-for-machine-learning-engineers/

Master CUDA: For Machine Learning Engineers

CUDA for Machine Learning: Practical Applications

Structure of a CUDA C/C++ application, where the host (CPU) code manages the execution of parallel code on the device (GPU).

Now that we’ve covered the basics, let’s explore how CUDA can be applied to common machine learning tasks.

Matrix Multiplication

Matrix multiplication is a fundamental operation in many machine learning algorithms, particularly in neural networks. CUDA can significantly accelerate this operation. Here’s a simple implementation:

__global__ void matrixMulKernel(float *A, float *B, float *C, int N) int row = blockIdx.y * blockDim.y + threadIdx.y; int col = blockIdx.x * blockDim.x + threadIdx.x; float sum = 0.0f; if (row < N && col < N) for (int i = 0; i < N; i++) sum += A[row * N + i] * B[i * N + col]; C[row * N + col] = sum; // Host function to set up and launch the kernel void matrixMul(float *A, float *B, float *C, int N) dim3 threadsPerBlock(16, 16); dim3 numBlocks((N + threadsPerBlock.x - 1) / threadsPerBlock.x, (N + threadsPerBlock.y - 1) / threadsPerBlock.y); matrixMulKernelnumBlocks, threadsPerBlock(A, B, C, N);

This implementation divides the output matrix into blocks, with each thread computing one element of the result. While this basic version is already faster than a CPU implementation for large matrices, there’s room for optimization using shared memory and other techniques.

Convolution Operations

Convolutional Neural Networks (CNNs) rely heavily on convolution operations. CUDA can dramatically speed up these computations. Here’s a simplified 2D convolution kernel:

__global__ void convolution2DKernel(float *input, float *kernel, float *output, int inputWidth, int inputHeight, int kernelWidth, int kernelHeight) int x = blockIdx.x * blockDim.x + threadIdx.x; int y = blockIdx.y * blockDim.y + threadIdx.y; if (x < inputWidth && y < inputHeight) float sum = 0.0f; for (int ky = 0; ky < kernelHeight; ky++) for (int kx = 0; kx < kernelWidth; kx++) int inputX = x + kx - kernelWidth / 2; int inputY = y + ky - kernelHeight / 2; if (inputX >= 0 && inputX < inputWidth && inputY >= 0 && inputY < inputHeight) sum += input[inputY * inputWidth + inputX] * kernel[ky * kernelWidth + kx]; output[y * inputWidth + x] = sum;

This kernel performs a 2D convolution, with each thread computing one output pixel. In practice, more sophisticated implementations would use shared memory to reduce global memory accesses and optimize for various kernel sizes.

Stochastic Gradient Descent (SGD)

SGD is a cornerstone optimization algorithm in machine learning. CUDA can parallelize the computation of gradients across multiple data points. Here’s a simplified example for linear regression:

__global__ void sgdKernel(float *X, float *y, float *weights, float learningRate, int n, int d) int i = blockIdx.x * blockDim.x + threadIdx.x; if (i < n) float prediction = 0.0f; for (int j = 0; j < d; j++) prediction += X[i * d + j] * weights[j]; float error = prediction - y[i]; for (int j = 0; j < d; j++) atomicAdd(&weights[j], -learningRate * error * X[i * d + j]); void sgd(float *X, float *y, float *weights, float learningRate, int n, int d, int iterations) int threadsPerBlock = 256; int numBlocks = (n + threadsPerBlock - 1) / threadsPerBlock; for (int iter = 0; iter < iterations; iter++) sgdKernel<<<numBlocks, threadsPerBlock>>>(X, y, weights, learningRate, n, d);

This implementation updates the weights in parallel for each data point. The atomicAdd function is used to handle concurrent updates to the weights safely.

Optimizing CUDA for Machine Learning

While the above examples demonstrate the basics of using CUDA for machine learning tasks, there are several optimization techniques that can further enhance performance:

Coalesced Memory Access

GPUs achieve peak performance when threads in a warp access contiguous memory locations. Ensure your data structures and access patterns promote coalesced memory access.

Shared Memory Usage

Shared memory is much faster than global memory. Use it to cache frequently accessed data within a thread block.

Understanding the memory hierarchy with CUDA

This diagram illustrates the architecture of a multi-processor system with shared memory. Each processor has its own cache, allowing for fast access to frequently used data. The processors communicate via a shared bus, which connects them to a larger shared memory space.

For example, in matrix multiplication:

__global__ void matrixMulSharedKernel(float *A, float *B, float *C, int N) __shared__ float sharedA[TILE_SIZE][TILE_SIZE]; __shared__ float sharedB[TILE_SIZE][TILE_SIZE]; int bx = blockIdx.x; int by = blockIdx.y; int tx = threadIdx.x; int ty = threadIdx.y; int row = by * TILE_SIZE + ty; int col = bx * TILE_SIZE + tx; float sum = 0.0f; for (int tile = 0; tile < (N + TILE_SIZE - 1) / TILE_SIZE; tile++) if (row < N && tile * TILE_SIZE + tx < N) sharedA[ty][tx] = A[row * N + tile * TILE_SIZE + tx]; else sharedA[ty][tx] = 0.0f; if (col < N && tile * TILE_SIZE + ty < N) sharedB[ty][tx] = B[(tile * TILE_SIZE + ty) * N + col]; else sharedB[ty][tx] = 0.0f; __syncthreads(); for (int k = 0; k < TILE_SIZE; k++) sum += sharedA[ty][k] * sharedB[k][tx]; __syncthreads(); if (row < N && col < N) C[row * N + col] = sum;

This optimized version uses shared memory to reduce global memory accesses, significantly improving performance for large matrices.

Asynchronous Operations

CUDA supports asynchronous operations, allowing you to overlap computation with data transfer. This is particularly useful in machine learning pipelines where you can prepare the next batch of data while the current batch is being processed.

cudaStream_t stream1, stream2; cudaStreamCreate(&stream1); cudaStreamCreate(&stream2); // Asynchronous memory transfers and kernel launches cudaMemcpyAsync(d_data1, h_data1, size, cudaMemcpyHostToDevice, stream1); myKernel<<<grid, block, 0, stream1>>>(d_data1, ...); cudaMemcpyAsync(d_data2, h_data2, size, cudaMemcpyHostToDevice, stream2); myKernel<<<grid, block, 0, stream2>>>(d_data2, ...); cudaStreamSynchronize(stream1); cudaStreamSynchronize(stream2);

Tensor Cores

For machine learning workloads, NVIDIA’s Tensor Cores (available in newer GPU architectures) can provide significant speedups for matrix multiply and convolution operations. Libraries like cuDNN and cuBLAS automatically leverage Tensor Cores when available.

Challenges and Considerations

While CUDA offers tremendous benefits for machine learning, it’s important to be aware of potential challenges:

Memory Management: GPU memory is limited compared to system memory. Efficient memory management is crucial, especially when working with large datasets or models.

Data Transfer Overhead: Transferring data between CPU and GPU can be a bottleneck. Minimize transfers and use asynchronous operations when possible.

Precision: GPUs traditionally excel at single-precision (FP32) computations. While support for double-precision (FP64) has improved, it’s often slower. Many machine learning tasks can work well with lower precision (e.g., FP16), which modern GPUs handle very efficiently.

Code Complexity: Writing efficient CUDA code can be more complex than CPU code. Leveraging libraries like cuDNN, cuBLAS, and frameworks like TensorFlow or PyTorch can help abstract away some of this complexity.

As machine learning models grow in size and complexity, a single GPU may no longer be sufficient to handle the workload. CUDA makes it possible to scale your application across multiple GPUs, either within a single node or across a cluster.

CUDA Programming Structure

To effectively utilize CUDA, it’s essential to understand its programming structure, which involves writing kernels (functions that run on the GPU) and managing memory between the host (CPU) and device (GPU).

Host vs. Device Memory

In CUDA, memory is managed separately for the host and device. The following are the primary functions used for memory management:

cudaMalloc: Allocates memory on the device.

cudaMemcpy: Copies data between host and device.

cudaFree: Frees memory on the device.

Example: Summing Two Arrays

Let’s look at an example that sums two arrays using CUDA:

__global__ void sumArraysOnGPU(float *A, float *B, float *C, int N) int idx = threadIdx.x + blockIdx.x * blockDim.x; if (idx < N) C[idx] = A[idx] + B[idx]; int main() int N = 1024; size_t bytes = N * sizeof(float); float *h_A, *h_B, *h_C; h_A = (float*)malloc(bytes); h_B = (float*)malloc(bytes); h_C = (float*)malloc(bytes); float *d_A, *d_B, *d_C; cudaMalloc(&d_A, bytes); cudaMalloc(&d_B, bytes); cudaMalloc(&d_C, bytes); cudaMemcpy(d_A, h_A, bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_B, h_B, bytes, cudaMemcpyHostToDevice); int blockSize = 256; int gridSize = (N + blockSize - 1) / blockSize; sumArraysOnGPU<<<gridSize, blockSize>>>(d_A, d_B, d_C, N); cudaMemcpy(h_C, d_C, bytes, cudaMemcpyDeviceToHost); cudaFree(d_A); cudaFree(d_B); cudaFree(d_C); free(h_A); free(h_B); free(h_C); return 0;

In this example, memory is allocated on both the host and device, data is transferred to the device, and the kernel is launched to perform the computation.

Conclusion

CUDA is a powerful tool for machine learning engineers looking to accelerate their models and handle larger datasets. By understanding the CUDA memory model, optimizing memory access, and leveraging multiple GPUs, you can significantly enhance the performance of your machine learning applications.

[pinktrashgoblin]

”Flown across the ocean, leavin’ just a memory.”

/lyr

Mini comic ft. Some teasers towards Cuda’s backstory, shown through the eyes of his brother and nephew :]

Also took this as a chance to practice interior backgrounds without using blender models and I think they turned out good!

Also also this was just an excuse to draw Storme lmao

[kaiasky]

in general i feel like i understand OS bullshit pretty well but it all goes out the window with graphics libraries. like X/wayland is a userspace process. And like the standard model is that my process says "hey X Window System. how big is my window? ok please blit this bitmap to this portion of my window" and then X is like ok, and then it does the compositing and updates the framebuffer through some kernel fd or something

but presumably isn't *actually* compositing windows anymore because what if one of those windows is 3d, in which case that'll be handled by the GPU? so it seems pretty silly to like, grab a game's framebuffer from vram, load it into userspace memory, write it back out to vram for display? presumably the window just says 'hey x window system i am using openGL please blit me every frame" and then...

wait ok i guess i understand how it must work i think. ok so naturally the GPU doesn't know what the fuck a process is. so presumably there's some kernelspace thing that provides GPU memory isolation (and maybe virtualization?) which definitely exists because i got crashes in CUDA code from oob memory access. but in the abstract there's nothing to say it can't ignkre those restrictions in some cases?

and so ig the window compositor must run in like. some special elevated mode where it's allowed to query the kernel for "hey give me all of the other processes framebuffers"? or like OBS also has stuff for recording a window even if that window's occluded? so there must just be some state that can give a process the right to use other proc's gpu bufs?

the alternative is ig... some kind of way to pass framebuffers around (and part of being a X client is saying hi here's my framebuffer) . which ig if they are implemented as fd's with ioctl it'd be possible?

[govindhtech]

SYCL 2020’s Five New Features For Modern C++ Programmers

SYCL

For accelerator-using C++ programmers, SYCL 2020 is interesting. People enjoyed contributing to the SYCL standard, a book, and the DPC++ open source effort to integrate SYCL into LLVM. The SYCL 2020 standard included some of the favorite new features. These are Intel engineers’ views, not Khronos’.

Khronos allows heterogeneous C++ programming with SYCL. After SYCL 2020 was finalized in late 2020, compiler support increased.

SYCL is argued in several places, including Considering a Heterogeneous Future for C++ and other materials on sycl.tech. How will can allow heterogeneous C++ programming with portability across vendors and architectures? SYCL answers that question.

SYCL 2020 offers interesting new capabilities to be firmly multivendor and multiarchitecture with to community involvement.

The Best Five

A fundamental purpose of SYCL 2020 is to harmonize with ISO C++, which offers two advantages. First, it makes SYCL natural for C++ programmers. Second, it lets SYCL test multivendor, multiarchitecture heterogeneous programming solutions that may influence other C++ libraries and ISO C++.

Changing the base language from C++11 to C++17 allows developers to use class template argument deduction (CTAD) and deduction guides, which necessitated several syntactic changes in SYCL 2020.

Backends allow SYCL to target more hardware by supporting languages/frameworks other than OpenCL.

USM is a pointer-based alternative to SYCL 1.2.1’s buffer/accessor concept.

A “built-in” library in SYCL 2020 accelerates reductions, a frequent programming style.

The group library abstracts cooperative work items, improving application speed and programmer efficiency by aligning with hardware capabilities (independent of vendor).

Atomic references aligned with C++20 std::atomic_ref expand heterogeneous device memory models.

These enhancements make the SYCL ecosystem open, multivendor, and multiarchitecture, allowing C++ writers to fully leverage heterogeneous computing today and in the future.

Backends

With backends, SYCL 2020 allows implementations in languages/frameworks other than OpenCL. Thus, the namespace has been reduced to sycl::, and the SYCL header file has been relocated from to .

These modifications affect SYCL deeply. Although implementations are still free to build atop OpenCL (and many do), generic backends have made SYCL a programming approach that can target more diverse APIs and hardware. SYCL can now “glue” C++ programs to vendor-specific libraries, enabling developers to target several platforms without changing their code.

SYCL 2020 has true openness, cross-architecture, and cross-vendor.

This flexibility allows the open-source DPC++ compiler effort to support NVIDIA, AMD, and Intel GPUs by implementing SYCL 2020 in LLVM (clang). SYCL 2020 has true openness, cross-architecture, and cross-vendor.

Unified shared memory

Some devices provide CPU-host memory unified views. This unified shared memory (USM) from SYCL 2020 allows a pointer-based access paradigm instead of the buffer/accessor model from SYCL 1.2.1.

Programming with USM provides two benefits. First, USM provides a single address space across host and device; pointers to USM allocations are consistent and may be provided to kernels as arguments. Porting pointer-based C++ and CUDA programs to SYCL is substantially simplified. Second, USM allows shared allocations to migrate seamlessly between devices, enhancing programmer efficiency and compatibility with C++ containers (e.g., std::vector) and algorithms.

Three USM allocations provide programmers as much or as little data movement control as they want. Device allocations allow programmers full control over application data migration. Host allocations are beneficial when data is seldom utilized and transporting it is not worth the expense or when data exceeds device capacity. Shared allocations are a good compromise that immediately migrate to use, improving performance and efficiency.

Reductions

Other C++ reduction solutions, such as P0075 and the Kokkos and RAJA libraries, influenced SYCL 2020.

The reducer class and reduction function simplify SYCL kernel variable expression using reduction semantics. It also lets implementations use compile-time reduction method specialization for good performance on various manufacturers’ devices.

The famous BabelStream benchmark, published by the University of Bristol, shows how SYCL 2020 reductions increase performance. BabelStream’s basic dot product kernel computes a floating-point total of all kernel work items. The 43-line SYCL 1.2.1 version employs a tree reduction in work-group local memory and asks the user to choose the optimal device work-group size. SYCL 2020 is shorter (20 lines) and more performance portable by leaving algorithm and work-group size to implementation.

Group Library

The work-group abstraction from SYCL 1.2.1 is expanded by a sub-group abstraction and a library of group-based algorithms in SYCL 2020.

Sub_group describes a kernel’s cooperative work pieces running “together,” providing a portable abstraction for various hardware providers. Sub-groups in the DPC++ compiler always map to a key hardware concept SIMD vectorization on Intel architectures, “warps” on NVIDIA architectures, and “wavefronts” on AMD architectures enabling low-level performance optimization for SYCL applications.

In another tight agreement with ISO C++, SYCL 2020 includes group-based algorithms based on C++17: all_of, any_of, none_of, reduce, exclusive_scan, and inclusive_scan. SYCL implementations may use work-group and/or sub-group parallelism to produce finely tailored, cooperative versions of these functions since each algorithm is supported at various scopes.

Atomic references

Atomics improved in C++20 with the ability to encapsulate types in atomic references (std::atomic_ref). This design (sycl::atomic_ref) is extended to enable address spaces and memory scopes in SYCL 2020, creating an atomic reference implementation ready for heterogeneous computing.

SYCL follows ISO C++, and memory scopes were necessary for portable programming without losing speed. Don’t disregard heterogeneous systems’ complicated memory topologies.

Memory models and atomics are complicated, hence SYCL does not need all devices to use the entire C++ memory model to support as many devices as feasible. SYCL offers a wide range of device capabilities, another example of being accessible to all vendors.

Beyond SYCL 2020: Vendor Extensions

SYCL 2020’s capability for multiple backends and hardware has spurred vendor extensions. These extensions allow innovation that provides practical solutions for devices that require it and guides future SYCL standards. Extensions are crucial to standardization, and the DPC++ compiler project’s extensions inspired various elements in this article.

Two new DPC++ compiler features are SYCL 2020 vendor extensions.

Group-local Memory at Kernel Scope

Local accessors in SYCL 1.2.1 allow for group-local memory, which must be specified outside of the kernel and sent as a kernel parameter. This might seem weird for programmers from OpenCL or CUDA, thus has created an extension to specify group-local memory in a kernel function. This improvement makes kernels more self-contained and informs compiler optimizations (where local memory is known at compile-time).

FPGA-Specific Extensions

The DPC++ compiler project supports Intel FPGAs. It seems that the modifications, or something similar, can work with any FPGA suppliers. FPGAs are a significant accelerator sector, and nous believe it pioneering work will shape future SYCL standards along with other vendor extension initiatives.

Have introduced FPGA choices to make buying FPGA hardware or emulation devices easier. The latter allows quick prototyping, which FPGA software writers must consider. FPGA LSU controls allow us to tune load/store operations and request a specific global memory access configuration. Also implemented data placement controls for external memory banks (e.g., DDR channel) to tune FPGA designs via FPGA memory channel. FPGA registers allow major tuning controls for FPGA high-performance pipelining.

Summary

Heterogeneity endures. Many new hardware alternatives focus on performance and performance-per-watt. This trend will need open, multivendor, multiarchitecture programming paradigms like SYCL.

The five new SYCL 2020 features assist provide portability and performance mobility. C++ programmers may maximize heterogeneous computing with SYCL 2020.

Read more on Govindhtech.com

[ciotechviews]

On Thursday, AMD officially released its newest AI chip: Instinct MI325X. The company has positioned this product directly to take on Nvidia’s dominant data center GPUs. The MI325X pits against the upcoming Blackwell chips from Nvidia, whose shipment will begin early next year. This is AMD’s strategic move to seize more considerable shares of the booming AI chip space that Bloomberg places at $500 billion by 2028.

AMD has always been second in the race in data centre GPU. For the MI325X, the company is looking to force the hand of Nvidia. After all, Nvidia currently enjoys over 90% market share here. This could shake the price of Nvidia’s products, which have been enjoying high margins with the soaring demand for its GPUs, largely because of AI applications. Chief among them is OpenAI’s ChatGPT.

The demand for AI has been higher than expected and investments grow super-duper quickly in the industry,” said CEO Lisa Su. AMD did not introduce new major cloud customers, but it does partner with Meta and Microsoft and it is so far supplying its AI chips in some OpenAI applications.

The biggest challenge AMD is facing currently is Nvidia’s proprietary CUDA programming language, which has become the new standard for AI developers. To avoid getting locked into Nvidia’s own ecosystem, AMD has been working on increasing capabilities of ROCm software to make it easy for developers who are already on the Nvidia side. The MI325X advances its performance to up to 40% more than that of the Nvidia H200 by running Meta’s Llama AI models with much advanced memory.

The company also debuted its 5th Gen EPYC CPUs, further solidifying its place within the data center. These announcements suggest that AMD is planning to go head-to-head with both Nvidia and Intel rather assertively in the AI and data center spaces.

[buysellram]

Asking ChatGPT Questions on GPU Graphics Card

What is ChatGPT? Before jumping to our topic, we can look at how ChatGPT introduces itself: “ChatGPT is a large language model that was trained by OpenAI. It is designed to be able to generate human-like text in response to prompts given to it, and it is capable of doing so in a variety of different styles and formats. It is based on the GPT-3 (Generative Pretrained Transformer 3) architecture, which is a type of machine learning model that uses deep learning algorithms to generate text. ChatGPT is designed to be able to generate conversational text that is similar to what a human might say in response to a given prompt, making it well-suited for applications such as chatbots and natural language processing tasks.”

What do you think? This sounds like a natural conversation. In fact, ChatGPT is a new AI-driven chatbot that can answer some questions and even write a paragraph of essays. When we search on Google for an answer, the search engine tries to retrieve webpages with higher matching scores to the questions. We need to check each webpage for a satisfactory answer. As humans write the content of webpages, the answer can be a good candidate. Unlike Google search, ChatGPT is trained with all webpage contents crawled from the internet. It learns the style of answering a question. Meanwhile, we can consider the content of an answer as the summarization of the major answers online. Hence, sometimes the answer can be wrong even though it reads like someone confidently answers a question. It is still too early to say it can answer questions with strong logic.

​As we are a computer hardware company, let us start to “interview” ChatGPT with questions on GPU graphics cards. From the answers, we believe in the future, we can have a smart chatbot online to do customer service since most answers sound reasonable.

Question: Is it a good idea to buy a used GPU?

ChatGPT: It can be a good idea to buy a used GPU, but it’s important to be cautious and do your research. Here are a few things to consider when buying a used GPU:

Condition: Make sure the GPU is in good working condition. Check for any visible damage, such as bent pins or cracks in the PCB.

Compatibility: Make sure the GPU is compatible with your computer’s motherboard and power supply. Check the manufacturer’s website for the minimum requirements for the GPU.

Performance: Check the specs of the used GPU and compare it to a new one. You may be able to find a used GPU that performs just as well as a new one, but at a lower price.

Warranty: Consider whether the used GPU comes with a warranty or return policy. Some sellers may offer a limited warranty or return policy, which can give you some peace of mind.

Overall, it’s important to do your research and be cautious when buying a used GPU. It can be a good way to save money, but it’s important to make sure you’re getting a quality product.

Q: What kinds of GPUs are good for 4K PC gaming, and what kinds of GPUs are good for AI computing?

ChatGPT: For 4K PC gaming, you’ll want a GPU with a high amount of memory and a fast clock speed. Some examples of GPUs that are good for 4K gaming include the NVIDIA GeForce RTX 3080, the AMD Radeon RX 6800 XT, and the NVIDIA GeForce RTX 3090. These GPUs offer a high level of performance and can handle the demands of 4K gaming.

For AI computing, you’ll want a GPU with a high amount of memory and a fast clock speed, as well as support for parallel processing. Some examples of GPUs that are good for AI computing include the NVIDIA Tesla V100, the NVIDIA Quadro GV100, and the NVIDIA GeForce RTX 8000. These GPUs are specifically designed for AI and machine learning applications, and offer a high level of performance for these types of tasks.

It’s worth noting that the specific GPU you choose will depend on your specific needs and budget. There are many other factors to consider, such as the amount of memory, the number of CUDA cores, and the memory bandwidth. It’s important to do your research and choose the GPU that best meets your needs.

Question: Please recommend some good GPUs for 4K PC gaming in the year 2022

ChatGPT: In 2022, some good GPUs for 4K PC gaming are likely to include:

NVIDIA GeForce RTX 3080: This GPU offers excellent performance and is capable of running most games at 4K resolution with high graphics settings. It features 10GB of memory and 8704 CUDA cores.

AMD Radeon RX 6800 XT: This GPU also offers excellent performance and is capable of running most games at 4K resolution with high graphics settings. It features 16GB of memory and 4608 stream processors.

NVIDIA GeForce RTX 3090: This is the most powerful GPU currently available, and is ideal for 4K gaming. It features 24GB of memory and 10496 CUDA cores, making it capable of running even the most demanding games at 4K resolution with high graphics settings.

It’s worth noting that the specific GPU you choose will depend on your budget and specific needs. There are many other factors to consider, such as the amount of memory, the number of CUDA cores, and the memory bandwidth. It’s important to do your research and choose the GPU that best meets your needs.

Question: How about the year of 2023?

ChatGPT: It’s difficult to predict exactly what the best GPUs for 4K PC gaming in 2023 will be, as new products are constantly being released and technology is constantly improving. However, some factors that are likely to influence the performance of GPUs in 2023 include the number of CUDA cores, the amount of memory, and the clock speed.

In general, you’ll want to look for a GPU with a high number of CUDA cores, a large amount of memory, and a fast clock speed in order to get the best performance for 4K gaming. It’s also a good idea to keep an eye on the latest GPU releases from companies like NVIDIA and AMD, as they often release new products that offer improved performance and features.

Ultimately, the best GPU for 4K gaming in 2023 will depend on your budget and specific needs. It’s important to do your research and choose the GPU that best meets your needs.

Questions: How many GPUs have been employed to train ChatGPT?

ChatGPT: I’m sorry, but I don’t have information about the specific hardware used to train ChatGPT. ChatGPT is a large language model developed by OpenAI, and it’s likely that it was trained using a combination of CPU and GPU hardware. Training large language models like ChatGPT typically requires a lot of computational resources, including a large number of CPUs and GPUs. However, I don’t have specific details about the hardware used to train ChatGPT.

Conclusion: ChatGPT will deeply affect many areas, such as all QA websites, HowTo websites, and even online search and programming. Some people have demonstrated that ChatGPT can generate some basic AI framework to finish a simple AI task. It will change education in the future too. Students are not trained to memory knowledge, but how to ask the right questions, which is a very important capability. ChatGPT can also help small businesses. It can be employed as an online chatbot to answer questions from customers. For example, as an ITAD company, BuySellRam.com receives customer queries daily, so we may leverage an AI chatbot to help customers to get a quote or to solve common computer problems.

Originally published at https://www.buysellram.com on December 18, 2022.

[sanaavay]

Features: 2nd Gen Ray Tracing Cores 3rd Gen Tensor Cores Microsoft® DirectX® 12 Ultimate GDDR6X Graphics Memory NVIDIA DLSS NVIDIA® GeForce Experience™ NVIDIA G-SYNC® NVIDIA GPU Boost™ Game Ready Drivers Vulkan RT API, OpenGL 4.6 DisplayPort 1.4a, HDMI 2.1 HDCP 2.3 VR Ready NVIDIA® NVLink® (SLI Ready) 1-Click OC All photos, specifications, contents are used for reference only and are subject to change without notice. Reaching another new height with superior performance in the RTX™ 30 series family, the latest GeForce RTX™ 3090 Ti EX Gamer takes a great leap from the previous RTX™ 3090 EX Gamer model, featuring a record-breaking 10,752 CUDA cores and the max board power of 450W. In addition to a power boost to the CUDA cores and max board power, the brand-new GeForce RTX™ 3090 Ti EX Gamer graphics card emerges with an all-new power connector, fans, and cooling system ensuring an even greater balance between performance and heat dissipation efficiency, fulfilling the needs of hardcore gamers and overclocking enthusiasts worldwide. 1-Click OC allows you to boost your graphics cards with just one click! Download Xtreme Tuner now! Customize your RGB color with Xtreme Tuner, or synchronize with the rest of your system, by connecting the graphics card to +12V RGB header of your motherboard or other RGB control system, using the included cable.

[fahrni]

Saturday Morning Coffee

Good morning from Charlottesville, Virginia! ☕️

I’ve been a bit obsessed with the idea of creating a CAD package for the Mac recently. For the challenge of it is why, but it would only be doable in a decent amount of time with financial backing large enough to hire a few folks to pull it off.

There is a way to jumpstart the process. The Open Design Alliance has portable libraries for reading and writing DWG files as well as rendering and so much more. All in portable C++.

Imagine a beautiful CAD app created just for the Mac. And yes, I know many already exist. 😁

Oh, right, I have a Mac app I need to finish.

Well, let’s get to it! Enjoy the links.

NBC News

DNC 2024 highlights: Kamala Harris accepts historic nomination in speech capping Democratic convention

We have our nominee! Now, let’s push her across the finish line and get our first Madame President!

Marc Palmer • Shareshot

Today we launched Shareshot! We’ve been working on this app for almost exactly a year, and we’re so pleased to be able to finally ship it. Here’s a little backstory and behind-the-scenes for those of you into app development.

Congratulations, Marc! Shareshot is a beautiful example of iOS craftsmanship. Go give it a try!

Alex Gaynor

I am an unrepentant advocate for migrating away from memory-unsafe languages (C and C++) to memory safe languages in security-relevant contexts. Many people reply that migrating large code bases to new languages is expensive, and we’d be better off making C++ safer. This is a reasonable response, after all there’s an enormous amount of C++ in the wild.

There is an enormous amount of C and C++ in the world. Too much to simply replace. I like Alex’s pragmatism on the matter. He has some proposals to improve the language without taking it too far down the path to incompatibility.

Just this week my interest in Rust began to grow. I’ve been using Swift daily since 2014, maybe 2015, and I really love the language and its ability to leverage the compiler to fix many of the memory issues seen in C and C++, like dangling pointers, forgotten allocations, and object lifetimes. We also have Rust to provide us with a solid memory protection model and the ability to be used for high performance code that is cross platform.

Rewriting software is costly and can also cost you your company. So taking that on should probably be avoided like the plague.

What if you picked your battles? How about writing new code in Rust or Swift? Perhaps improve public access to API’s by fronting it with Rust? How about picking some code known to cause a lot of crashes in your app and rewrite just that bit?

We can use tried and true methods in C++ to improve memory safety but it requires developers to be extremely disciplined.

Simple things like filling new memory allocations with known patterns. I prefer to fill the memory with zeros. You can also do the same when you delete it.

Reference counted pointers — AKA smart pointers — help.

Modern C++ has introduced mechanisms to transfer pointer ownership, always a tough problem to handle and the problem that lead to the creation of smart pointers.

Anywho, the piece is an easy read with good ideas. Go give it a gander.

Jess Weatherbed • The Verge

Many Procreate users can breathe a sigh of relief now that the popular iPad illustration app has taken a definitive stance against generative AI. “We’re not going to be introducing any generative AI into our products,” Procreate CEO James Cuda said in a video posted to X. “I don’t like what’s happening to the industry, and I don’t like what it’s doing to artists.”

I can really appreciate this stance. Artists often have a deep psychological attachment to their work and the creative process — hell — they go through to bring it to life. Taking that work, that style, and using it to train an AI to rip them off is just slimy.

Caleb Newton • Bipartisan Report

A dozen individuals who served as lawyers in Republican presidential administrations are bucking Republican presidential nominee Donald Trump and endorsing Democratic presidential pick Kamala Harris in a new letter that was publicized first at Fox News. The list includes prominent former judge J. Michael Luttig, who also served in the Reagan Administration.

Even with all of this at least half the country will vote for the Orange Man. It’s shocking, really.

Joe Brockmeier • lwn.net

The FreeBSD Project is, for the second time this year, engaging in a long-running discussion about the possibility of including Rust in its base system. The sequel to the first discussion included some work by Alan Somers to show what it might look like to use Rust code in the base tree. Support for Rust code does not appear much closer to being included in FreeBSD’s base system, but the conversation has been enlightening.

Speaking of Rust! Apparently Rust has found its way into the Linux Kernel and Microsoft has used it for Windows API development. It’s time for FreeBSD to get on board!

I wonder if Apple with push some Swift into Darwin or XNU at some point? Swift was written so it could be used for system level programming.

Carole Cadwalladr • The Guardian

Inciting rioters in Britain was a test run for Elon Musk. Just see what he plans for America

Musk has gone deep down the MAGA rabbit hole. His ketamine addled brain lives in its own world of conspiracies and white supremacy.

He’s unraveling in real time. Dumping his, often wacko, thoughts on X. He behaves more like a two year old than a man in his 50s.

Why do people still believe this man is some kind of genius? He’s a man child who throws hissy fits until he gets what he wants.

Money can’t buy happiness but it can buy politicians.

Matt Birchler • Birchtree

Why does Apple feel it’s worth trashing their relationship with creators and developers so that they can take 30% of the money I pay an up-and-coming creator who is trying to make rent in time each month? This isn’t a hypothetical, I genuinely want to know. Is the goal to turn into Microsoft, because this is how you turn into Microsoft.

Hate to say it Matt but Apple is today what Microsoft was in the 90’s. They are the 800lb gorilla in the room throwing their weight around.

I really love Apple products and their development tools and can’t see switching away from them. I just wish they’d be a bit kinder to the development community, that’s all.

Kelly Dobkin • Los Angeles Times

chef and co-owner Eric Park serves a black sesame misugaru drink that combines espresso, oat milk, the multigrain powder and gets topped with black sesame cream. It’s nutty, sweet and frothy, but not too rich thanks to the bitterness of the espresso.

Ok, now I really want to try misugaru. The one described above sounds incredible. 🤤

Alex Henderson • Raw Story

Reading through the Ohio Revised Code, Case Western Reserve University Law Professor Atiba Ellis couldn’t help looking for an alternative interpretation. Was there an error? Shoddy drafting? Because why on earth, he wondered, would a person clear that third bar, and submit documentation proving they broke the law by registering to vote?

This is just another GOP scheme to kick people off voter rolls. 🤬

Foone

ahh, another startup that burnt out trying to build some silly AI project on crap hardware. I wonder what they did? I check their URL: ahh. healthcare. great, great.

This Mastodon thread is an interesting read and a cautionary tale. Before you sale off old hardware make sure you remove its storage or at the very least wipe the storage with a destructive reformat.

Zarar

Around 2AM this morning I had a realization that this was the most stressed I have ever been. On verge of a complete breakdown.

Ahhh, the life of a software developer. I’ve seen and experienced this stress on numerous occasions. I don’t recommend it.

Daryl Baxter • iMore

This MacBook app generated $100,000 in seven days, now Stripe won’t pay up

This is a wild story and I hope the developer is able to get paid and save his company.

[smnet]

Card đồ họa NVIDIA T600 4GB 4mDP GFX - 340K9AA

CẠC ĐỒ HỌA HP NVIDIA T600 4GB 4MDP GFX_340K9AA Model T600 GPU Memory 4 GB GDDR6 Memory Interface 128-bit Memory Bandwidth Up to 160 GB/s NVIDIA CUDA Cores 640 Single-Precision Performance Up to 1.7 TFLOPs System Interface PCI Express 3.0 x 16 Max Power Consumption 40 W Thermal Solution Active Form Factor 2.713 inches H x 6.137 inches L , single slot Display Connectors 4 x mDP 1.4 with…

[omarvektrapc12]

Model NameInno3D Geforce RTX 4080 Super Ichill x3/ 16GB GDDR6XModel NumberC408S3-166XX-187049HBrandINNO3D

GPU Engine Specs: CUDA Cores10240Boost Clock (MHz)2610Base Clock(MHz)2295Thermal and Power Spec: Minimum System Power Requirement (W)750Supplementary Power Connectors3x PCIe 8-pin cables (adapter in box) OR 320 W or greater PCIe Gen 5 cableMemory Specs: Memory Clock23GbpsStandard Memory Config16GBMemory InterfaceGDDR6XMemory Interface Width256-bitMemory Bandwidth (GB/sec)736

[exeton]

NVIDIA H100 vs. A100: Which GPU Reigns Supreme?

NVIDIA’s CEO, Jensen Huang, unveiled the NVIDIA H100 Tensor Core GPU at NVIDIA GTC 2022, marking a significant leap in GPU technology with the new Hopper architecture. But how does it compare to its predecessor, the NVIDIA A100, which has been a staple in deep learning? Let’s explore the advancements and differences between these two powerhouse GPUs.

NVIDIA H100: A Closer Look

The NVIDIA H100, based on the new Hopper architecture, is NVIDIA’s ninth-generation data center GPU, boasting 80 billion transistors. Marketed as “the world’s largest and most powerful accelerator,” it’s designed for large-scale AI and HPC models. Key features include:

Most Advanced Chip: The H100 is built with cutting-edge technology, making it highly efficient for complex tasks.

New Transformer Engine: Enhances network speeds by six times compared to previous versions.

Confidential Computing: Ensures secure processing of sensitive data.

2nd-Generation Secure Multi-Instance GPU (MIG): Extends capabilities by seven times over the A100.

4th-Generation NVIDIA NVLink: Connects up to 256 H100 GPUs with nine times the bandwidth.

New DPX Instructions: Accelerates dynamic programming by up to 40 times compared to CPUs and up to seven times compared to previous-generation GPUs.

NVIDIA asserts that the H100 and Hopper technology will drive future AI research, supporting massive AI models, deep recommender systems, genomics, and complex digital twins. Its enhanced AI inference capabilities cater to real-time applications like giant-scale AI models and chatbots.

NVIDIA A100: A Deep Dive

Introduced in 2020, the NVIDIA A100 Tensor Core GPU was heralded as the highest-performing elastic data center for AI, data analytics, and HPC. Based on the Ampere architecture, it delivers up to 20 times higher performance than its predecessor. The A100’s notable features include:

Multi-Instance GPU (MIG): Allows division into seven GPUs, adjusting dynamically to varying demands.

Third-Generation Tensor Core: Boosts throughput and supports a wide range of DL and HPC data types.

The A100’s ability to support cloud service providers (CSPs) during the digital transformation of 2020 and the pandemic was crucial, delivering up to seven times more GPU instances through its MIG virtualization and GPU partitioning capabilities.

Architecture Comparison NVIDIA Hopper

Named after the pioneering computer scientist Grace Hopper, the Hopper architecture significantly enhances the MIG capabilities by up to seven times compared to the previous generation. It introduces features that improve asynchronous execution, allowing memory copies to overlap with computation and reducing synchronization points. Designed to accelerate the training of Transformer models on H100 GPUs by six times, Hopper addresses the challenges of long training periods for large models while maintaining GPU performance.

NVIDIA Ampere

Described as the core of the world’s highest-performing elastic data centers, the Ampere architecture supports elastic computing at high acceleration levels. It’s built with 54 billion transistors, making it the largest 7nm chip ever created. Ampere offers L2 cache residency controls for data management, enhancing data center scalability. The third generation of NVLink® in Ampere doubles GPU-to-GPU bandwidth to 600 GB/s, facilitating large-scale application performance.

Detailed Specifications: H100 vs. A100

H100 Specifications:

8 GPCs, 72 TPCs (9 TPCs/GPC), 2 SMs/TPC, 144 SMs per full GPU

128 FP32 CUDA Cores per SM, 18432 FP32 CUDA Cores per full GPU

4 Fourth-Generation Tensor Cores per SM, 576 per full GPU

6 HBM3 or HBM2e stacks, 12 512-bit Memory Controllers

60MB L2 Cache

Fourth-Generation NVLink and PCIe Gen 5

Fabricated on TSMC’s 4N process, the H100 has 80 billion transistors and 395 billion parameters, providing up to nine times the speed of the A100. It’s noted as the first truly asynchronous GPU, extending A100’s asynchronous transfers across address spaces and growing the CUDA thread group hierarchy with a new level called the thread block cluster.

A100 Specifications:

8 GPCs, 8 TPCs/GPC, 2 SMs/TPC, 16 SMs/GPC, 128 SMs per full GPU

64 FP32 CUDA Cores/SM, 8192 FP32 CUDA Cores per full GPU

4 third-generation Tensor Cores/SM, 512 third-generation Tensor Cores per full GPU

6 HBM2 stacks, 12 512-bit memory controllers

The A100 is built on the A100 Tensor Core GPU SM architecture and the third-generation NVIDIA high-speed NVLink interconnect. With 54 billion transistors, it delivers five petaflops of performance, a 20x improvement over its predecessor, Volta. The A100 also includes fine-grained structured sparsity to double the compute throughput for deep neural networks.

Conclusion

When comparing NVIDIA’s H100 and A100 GPUs, it’s clear that the H100 brings substantial improvements and new features that enhance performance and scalability for AI and HPC applications. While the A100 set a high standard in 2020, the H100 builds upon it with advanced capabilities that make it the preferred choice for cutting-edge research and large-scale model training. Whether you choose the H100 or A100 depends on your specific needs, but for those seeking the latest in GPU technology, the H100 is the definitive successor.

FAQs

1- What is the main difference between NVIDIA H100 and A100?

The main difference lies in the architecture and capabilities. The H100, based on the Hopper architecture, offers enhanced performance, scalability, and new features like the Transformer Engine and advanced DPX instructions.

2- Which GPU is better for deep learning: H100 or A100?

The H100 is better suited for deep learning due to its advanced features and higher performance metrics, making it ideal for large-scale AI models.

3- Can the H100 GPU be used for gaming?

While the H100 is primarily designed for AI and HPC tasks, it can theoretically be used for gaming, though it’s not optimized for such purposes.

4- What are the memory specifications of the H100 and A100?

The H100 includes six HBM3 or HBM2e stacks with 12 512-bit memory controllers, whereas the A100 has six HBM2 stacks with 12 512-bit memory controllers.

5- How does the H100’s NVLink compare to the A100's?

The H100 features fourth-generation NVLink, which connects up to 256 GPUs with nine times the bandwidth, significantly outperforming the A100’s third-generation NVLink.

Muhammad Hussnain Facebook | Instagram | Twitter | Linkedin | Youtube

[jcmarchi]

Huawei’s Ascend 910C: A Bold Challenge to NVIDIA in the AI Chip Market

New Post has been published on https://thedigitalinsider.com/huaweis-ascend-910c-a-bold-challenge-to-nvidia-in-the-ai-chip-market/

Huawei’s Ascend 910C: A Bold Challenge to NVIDIA in the AI Chip Market

The Artificial Intelligence (AI) chip market has been growing rapidly, driven by increased demand for processors that can handle complex AI tasks. The need for specialized AI accelerators has increased as AI applications like machine learning, deep learning, and neural networks evolve.

NVIDIA has been the dominant player in this domain for years, with its powerful Graphics Processing Units (GPUs) becoming the standard for AI computing worldwide. However, Huawei has emerged as a powerful competitor with its Ascend series, leading itself to challenge NVIDIA’s market dominance, especially in China. The Ascend 910C, the latest in the line, promises competitive performance, energy efficiency, and strategic integration within Huawei’s ecosystem, potentially reshaping the dynamics of the AI chip market.

Background on Huawei’s Ascend Series

Huawei’s entry into the AI chip market is part of a broader strategy to establish a self-reliant ecosystem for AI solutions. The Ascend series began with the Ascend 310, designed for edge computing, and the Ascend 910, aimed at high-performance data centers. Launched in 2019, the Ascend 910 was recognized as the world’s most powerful AI processor, delivering 256 teraflops (TFLOPS) of FP16 performance.

Built on Huawei’s proprietary Da Vinci architecture, the Ascend 910 offers scalable and flexible computing capabilities suitable for various AI workloads. The chip’s emphasis on balancing power with energy efficiency laid the groundwork for future developments, leading to the improved Ascend 910B and the latest Ascend 910C.

The Ascend series is also part of Huawei’s effort to reduce dependence on foreign technology, especially in light of U.S. trade restrictions. By developing its own AI chips, Huawei is working toward a self-sufficient AI ecosystem, offering solutions that range from cloud computing to on-premise AI clusters. This strategy has gained traction with many Chinese companies, particularly as local firms have been encouraged to limit reliance on foreign technology, such as NVIDIA’s H20. This has created an opportunity for Huawei to position its Ascend chips as a viable alternative in the AI space.

The Ascend 910C: Features and Specifications

The Ascend 910C is engineered to offer high computational power, energy efficiency, and versatility, positioning it as a strong competitor to NVIDIA’s A100 and H100 GPUs. It delivers up to 320 TFLOPS of FP16 performance and 64 TFLOPS of INT8 performance, making it suitable for a wide range of AI tasks, including training and inference.

The Ascend 910C delivers high computational power, consuming around 310 watts. The chip is designed for flexibility and scalability, enabling it to handle various AI workloads such as Natural Language Processing (NLP), computer vision, and predictive analytics. Additionally, the Ascend 910C supports high bandwidth memory (HBM2e), essential for managing large datasets and efficiently training complex AI models. The chip’s software compatibility, including support for Huawei’s MindSpore AI framework and other platforms like TensorFlow and PyTorch, makes it easier for developers to integrate into existing ecosystems without significant reconfiguration.

Huawei vs. NVIDIA: The Battle for AI Supremacy

NVIDIA has long been the leader in AI computing, with its GPUs serving as the standard for machine learning and deep learning tasks. Its A100 and H100 GPUs, built on the Ampere and Hopper architectures, respectively, are currently the benchmarks for AI processing. The A100 can deliver up to 312 TFLOPS of FP16 performance, while the H100 offers even more robust capabilities. NVIDIA’s CUDA platform has significantly advanced, creating a software ecosystem that simplifies AI model development, training, and deployment.

Despite NVIDIA’s dominance, Huawei’s Ascend 910C aims to offer a competitive alternative, particularly within the Chinese market. The Ascend 910C performs similarly to the A100, with slightly better power efficiency. Huawei’s aggressive pricing strategy makes the Ascend 910C a more affordable solution, offering cost savings for enterprises that wish to scale their AI infrastructure.

However, the software ecosystem remains a critical area of competition. NVIDIA’s CUDA is widely adopted and has a mature ecosystem, while Huawei’s MindSpore framework is still growing. Huawei’s efforts to promote MindSpore, particularly within its ecosystem, are essential to convince developers to transition from NVIDIA’s tools. Despite this challenge, Huawei has been progressing by collaborating with Chinese companies to create a cohesive software environment supporting the Ascend chips.

Reports indicate that Huawei has started distributing prototypes of the Ascend 910C to major Chinese companies, including ByteDance, Baidu, and China Mobile. This early engagement suggests strong market interest, especially among companies looking to reduce dependency on foreign technology. As of last year, Huawei’s Ascend solutions were used to train nearly half of China’s top 70 large language models, demonstrating the processor’s impact and widespread adoption.

The timing of the Ascend 910C launch is significant. With U.S. export restrictions limiting access to advanced chips like NVIDIA’s H100 in China, domestic companies are looking for alternatives, and Huawei is stepping in to fill this gap. Huawei’s Ascend 910B has already gained traction for AI model training across various sectors, and the geopolitical environment is driving further adoption of the newer 910C.

While NVIDIA is projected to ship over 1 million H20 GPUs to China, generating around $12 billion in revenue, Huawei’s Ascend 910C is expected to generate $2 billion in sales this year. Moreover, companies adopting Huawei’s AI chips may become more integrated into Huawei’s broader ecosystem, deepening reliance on its hardware and software solutions. However, this strategy may also raise concerns among businesses about becoming overly dependent on one vendor.

Strategic Partnerships and Alliances

Huawei has made strategic partnerships to drive the adoption of the Ascend 910C. Collaborations with major tech players like Baidu, ByteDance, and Tencent have facilitated the integration of Ascend chips into cloud services and data centers, ensuring that Huawei’s chips are part of scalable AI solutions. Telecom operators, including China Mobile, have incorporated Huawei’s AI chips into their networks, supporting edge computing applications and real-time AI processing.

These alliances ensure that Huawei’s chips are standalone products and integral parts of broader AI solutions, making them more attractive to enterprises. Additionally, this strategic approach allows Huawei to promote its MindSpore framework, building an ecosystem that could rival NVIDIA’s CUDA platform over time.

Geopolitical factors have significantly influenced Huawei’s strategy. With U.S. restrictions limiting its access to advanced semiconductor components, Huawei has increased its investments in R&D and collaborations with domestic chip manufacturers. This focus on building a self-sufficient supply chain is critical for Huawei’s long-term strategy, ensuring resilience against external disruptions and helping the company to innovate without relying on foreign technologies.

Technical Edge and Future Outlook

The Ascend 910C has gained prominence with its strong performance, energy efficiency, and integration into Huawei’s ecosystem. It competes closely with NVIDIA’s A100 in several key performance areas. For tasks that require FP16 computations, like deep learning model training, the chip’s architecture is optimized for high efficiency, resulting in lower operational costs for large-scale use.

However, challenging NVIDIA’s dominance is no easy task. NVIDIA has built a loyal user base over the years because its CUDA ecosystem offers extensive development support. For Huawei to gain more market share, it must match NVIDIA’s performance and offer ease of use and reliable developer support.

The AI chip industry will likely keep evolving, with technologies like quantum computing and edge AI reshaping the domain. Huawei has ambitious plans for its Ascend series, with future models promising even better integration, performance, and support for advanced AI applications. By continuing to invest in research and forming strategic partnerships, Huawei aims to strengthen its foundations in the AI chip market.

The Bottom Line

In conclusion, Huawei’s Ascend 910C is a significant challenge to NVIDIA’s dominance in the AI chip market, particularly in China. The 910C’s competitive performance, energy efficiency, and integration within Huawei’s ecosystem make it a strong contender for enterprises looking to scale their AI infrastructure.

However, Huawei faces significant hurdles, especially competing with NVIDIA’s well-established CUDA platform. The success of the Ascend 910C will rely heavily on Huawei’s ability to develop a robust software ecosystem and strengthen its strategic partnerships to solidify its position in the evolving AI chip industry.

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[govindhtech]

AMD ROCm 6.2.3 Brings Llama 3 And SD 2.1 To Radeon GPUs

AMD ROCm 6.2.3

AMD recently published AMD ROCm 6.2.3, the most recent version of their open compute software that supports Radeon GPUs on native Ubuntu Linux systems. Most significantly, this latest edition enables developers to use Stable Diffusion (SD) 2.1 text-to-image capabilities in their AI programming and offers amazing inference performance with Llama 3 70BQ4.

AMD focused on particular features to speed up the development of generative AI after its last release with AMD ROCm 6.1. Using vLLM and Flash Attention 2, AMD ROCm 6.2 provides pro-level performance for Large Language Model inference. This release also includes beta support for the Triton framework, enabling more users to develop AI functionality on AMD hardware.

The following are AMD ROCm 6.2.3 for Radeon GPUs’ four main feature highlights:

The most recent version of Llama is officially supported by vLLM. AMD ROCm on Radeon with Llama 3 70BQ4 offers amazing inference performance.

Flash Attention 2 “Forward Enablement” is officially supported. Its purpose is to speed up inference performance and lower memory requirements.

Formally endorsing stable diffusion (SD) The SD text-to-image model can be integrated into your own AI development.

Triton Beta Support: Use the Triton framework to quickly and simply develop high-performance AI programs with little experience

Since its first 5.7 release barely a year ago, AMD ROCm support for Radeon GPUs has advanced significantly.

With version 6.0, it formally qualified the usage of additional Radeon GPUs, such as the Radeon PRO W7800 with 32GB, and greatly increased AMD ROCm’s capabilities by adding support for the widely used ONNX runtime.

Another significant milestone was reached with the release of AMD ROCm 6.1, where it declared official support for the TensorFlow framework and multi-GPU systems. It also granted beta access to Windows Subsystem for Linux (WSL 2), which is now officially eligible for use with 6.1.

The AMD ROCm 6.2.3 solution stack for Radeon GPUs is as follows:

Although Linux was the primary focus of AMD ROCm 6.2.3, WSL 2 support will be released shortly.

ROCm on Radeon for AI and Machine Learning development has had a fantastic year, and it is eager to keep collaborating closely with the community to improve its product stack and support its system builders in developing attractive on-premises, client-based solutions.

Evolution of AMD ROCm from version 5.7 to 6.2.3

From version 5.7 to 6.2.3, AMD ROCm (Radeon Open Compute) has made substantial improvements to performance, hardware support, developer tools, and deep learning frameworks. Each release’s main improvements are listed below:

AMD ROCm 5.7

Support for New Architectures: ROCm 5.7 included support for AMD’s RDNA 3 family. This release expanded the GPUs that can utilize ROCm for deep learning and HPC.

HIP Improvements: AMD’s HIP framework for running CUDA code on AMD GPUs was optimized to facilitate interoperability between ROCm-supported systems and CUDA-based workflows.

Deep Learning Framework Updates: TensorFlow and PyTorch were made more compatible and performant. These upgrades optimized AI workloads in multi-GPU setups.

Performance Optimizations: This version improved HPC task performance, including memory management and multi-GPU scaling.

AMD ROCm 6.0

Unified Memory Support: ROCm 6.0 fully supported unified memory, making CPU-GPU data transfers smoother. This feature improved memory management, especially for applications that often access these processors’ memory.

New Compiler Infrastructure: AMD enhanced the ROCm Compiler (LLVM-based) for greater performance and larger workload support. We wanted to boost deep learning, HPC, and AI efficiency.

ROCm 6.0 might target more GPUs, especially in HPC, due to improved scalability and RDNA and CDNA architecture compatibility.

New CUDA compatibility features were added to the HIP API in this edition. These changes let developers convert CUDA apps to ROCm.

AMD ROCm 6.1

Optimized AI/ML Framework Compatibility: ROCm 6.1 improved PyTorch and TensorFlow performance. This improved mixed precision training, which maximizes GPU utilization in deep learning.

Experimental HIP Tensor Cores support allowed AI models to use hardware-accelerated matrix operations. This improvement greatly accelerated matrix multiplication, which is essential for deep learning.

Expanded Container Support: AMD included pre-built Docker containers that were easier to connect with Kubernetes in ROCm 6.1, simplifying cloud and cluster deployment.

More efficient data transfer in multi-GPU systems was achieved by improving memory and I/O operations.

AMD ROCm 6.1.3

Support for several GPUs makes it possible to create scalable AI desktops for multi-user, multi-serving applications.

These solutions can be used with ROCm on a Windows OS-based system thanks to beta-level support for Windows Subsystem for Linux.

More options for AI development are provided via the TensorFlow Framework.

AMD ROCm 6.2

New Kernel and Driver Features: ROCm 6.2 improved low-level driver and kernel support for advanced computing workload stability and performance. This significantly strengthened ROCm in enterprise environments.

AMD Infinity Architecture Integration: ROCm 6.2 enhances AMD’s Infinity Architecture, which connects GPUs at fast speeds. Multi-GPU configurations performed better, especially for large-scale HPC and AI applications.

HIP API expansion: ROCm 6.2 improved the HIP API, making CUDA-based application conversion easier. Asynchronous data transmission and other advanced features were implemented in this release to boost computational performance.

AMD ROCm 6.2.3

The most recent version of Llama is officially supported by vLLM. AMD ROCm on Radeon with Llama 3 70BQ4 offers amazing inference performance.

Flash Attention 2 “Forward Enablement” is officially supported. Its purpose is to speed up inference performance and lower memory requirements.

Formally endorsing stable diffusion (SD) The SD text-to-image model can be integrated into your own AI development.

Triton Beta Support: Use the Triton framework to quickly and simply develop high-performance AI programs with little experience

Key Milestone Summary

These versions supported new AMD architectures (RDNA, CDNA) as ROCm expanded its hardware support.

CUDA application porting became easier with HIP updates, while data scientists and academics found AI/ML framework support more useful.

Multi-GPU Optimizations: Unified memory support, RDMA, and AMD Infinity Architecture improved multi-GPU deployments, which are essential for HPC and large-scale AI training.

Each release improved ROCm’s stability and scalability by fixing bugs, optimizing memory management, and improving speed.

AMD now prioritizes an open, high-performance computing platform for AI, machine learning, and HPC applications.

Read more on Govindhtech.com

[viperallc]

Manli NVIDIA RTX Turbo 4090 with Blower Cooling

In the ever-evolving world of graphics cards, a new titan has emerged: the Manli NVIDIA RTX Turbo 4090 Blower Cooling. This powerhouse is not just a component; it’s a monumental leap in gaming and creative performance. Let’s dive into its specifications and features that set it apart.

Specifications:

Product Name: Manli NVIDIA RTX Turbo 4090 Blower Cooling

Model Name: M-NRTX4090G/6RHHPPP-M3530

Chipset: GeForce RTX™ 4090

Base/Boost Clock: 2235/2520MHz

CUDA® Cores: 16384

Memory: 24GB GDDR6X, 21Gbps

Memory Interface: 384-bit

Memory Bandwidth: 1008GB/s

Width: 3.5-Slot

Cooling: Heatsink with Triple Cooler

Display Output: 3 x DisplayPort, HDMI

Dimensions: 351 x 145 x 63mm

Power: 450W

Max GPU Temperature: 90℃

Packaging Size: 439.5 x 229 x 112mm

Unparalleled Performance

Model Name: M-NRTX4090G/6RHHPPP-M3530. At the heart of this beast lies the GeForce RTX™ 4090 chipset, renowned for its supreme capabilities. The card boasts an impressive base and boost clock of 2235/2520MHz, ensuring that it performs exceptionally under demanding scenarios.

CUDA® Cores: With a staggering 16384 NVIDIA CUDA® Cores, the RTX 4090 is built for speed and efficiency, catering to the most intense gaming sessions and demanding creative workloads.

Next-Gen Memory

Memory Specs: Equipped with 24GB of GDDR6X memory and a memory speed of 21Gbps, this graphics card is designed for ultra-high-resolution gaming and complex 3D rendering tasks. The 384-bit memory interface and a bandwidth of 1008GB/s further underscore its capabilities in handling large data sets smoothly.

Cutting-Edge Cooling and Design

Cooling: The card’s innovative heatsink with Triple Cooler design ensures optimal thermal performance, crucial for maintaining stability and longevity under load.

Build: It’s a 3.5-slot card, with dimensions of 351 x 145 x 63mm, signifying a robust and sturdy build quality. The design is not just functional but also aesthetically pleasing, fitting well into any high-end gaming rig.

Connectivity and Power

Display Outputs: Connectivity is versatile with 3 x DisplayPort and HDMI options, allowing for multiple monitor setups or high-resolution displays.

Power Requirements: The graphics card power stands at 450W, and it operates safely up to a maximum temperature of 90℃.

Beyond Fast: The NVIDIA® GeForce RTX® 4090 Experience

The Manli NVIDIA RTX Turbo 4090 is more than just fast; it’s a revolution in graphics card technology. It is powered by the NVIDIA Ada Lovelace architecture, which brings significant improvements in performance, efficiency, and AI-powered graphics.

AI and Ray Tracing Performance: With the fourth-gen Tensor Cores and third-gen RT Cores, the card offers up to 2x AI and ray tracing performance compared to previous generations. This means more realistic lighting, shadows, and reflections in games, as well as faster rendering times for creators.

Ultimate Experience for Gamers and Creators: The combination of its powerful specs and advanced architecture makes the RTX 4090 a top choice for gamers who want to experience ultra-high-performance gaming and for creators involved in detailed virtual worlds, unprecedented productivity, and innovative content creation.

Conclusion

The Manli NVIDIA RTX Turbo 4090 Blower Cooling 24GB is a testament to what modern technology can achieve in the realm of graphics cards. It’s not just an upgrade; it’s a transformation that redefines what’s possible in gaming and creative computing. For those who demand the best, the RTX 4090 is undoubtedly the ultimate choice.

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As the hype around AI PCs grows, Nvidia brings a major refresh to RTX 40 series

Looking at the spec sheet, the somewhat confusingly named 4070 Ti Super (there are four notable 4070 SKUs) has a 300 MHz higher base clock and 768 more CUDA cores than the standard 4070 Ti up. Masu.

In fact, the biggest improvement is in memory. The 4070 Ti Super has 16 GB of GDDR6x memory connected to a wider 256-bit bus. An additional 4 gigabytes of memory may not sound like a big deal, but as generative AI applications become more common on devices, it's important to run larger and more accurate models. , more memory is essential.

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