baidu-research/warp-ctc
Fast parallel CTC.
gpus
NVIDIA Develops NVLink Switch: NVSwitch, 18 Ports For DGX-2 & More
NVLink Takes GPU Acceleration To The Next Level
One of the breakthrough moments in computing, which was compelled by necessity, was the advent of symmetric multiprocessor, or SMP, clustering to make two
Stacking Up AMD MI200 Versus Nvidia A100 Compute Engines
The modern GPU compute engine is a microcosm of the high performance computing datacenter at large. At every level of HPC – across systems in the
Survey paper on Deep Learning on GPUs
The rise of deep-learning (DL) has been fuelled by the improvements in accelerators. GPU continues to remain the most widely used accelerator for DL applications. We present a survey of architectur…
Fast Multi-GPU collectives with NCCL | NVIDIA Technical Blog
Today many servers contain 8 or more GPUs. In principle then, scaling an application from one to many GPUs should provide a tremendous performance boost. But in practice, this benefit can be difficult…
A Graph-based Model for GPU Caching Problems
Modeling data sharing in GPU programs is a challenging task because of the massive parallelism and complex data sharing patterns provided by GPU architectures. Better GPU caching efficiency can be...
https://blog.riseml.com/comparing-google-tpuv2-against-nvidia-v100-on-resnet-50-c2bbb6a51e5e
1804
http://research.baidu.com/bringing-hpc-techniques-deep-learning/
Beyond GPU Memory Limits with Unified Memory on Pascal | NVIDIA Technical Blog
Unified Memory on NVIDIA Pascal GPUs enables applications to run out-of-the-box with larger memory footprints and achieve great baseline performance.
A Look at Baidu’s Industrial-Scale GPU Training Architecture
Like its U.S. counterpart, Google, Baidu has made significant investments to build robust, large-scale systems to support global advertising programs. As
Mythic Resizes its AI Chip
Its second analog AI chip is optimized for different card sizes, but still aimed at computer vision workloads at the edge.
What Happens When Multipliers No Longer Define AI Accelerators?
Current custom AI hardware devices are built around super-efficient, high performance matrix multiplication. This category of accelerators includes the
How to Accelerate Signal Processing in Python
This post is the seventh installment of the series of articles on the RAPIDS ecosystem. The series explores and discusses various aspects of RAPIDS that allow its users solve ETL (Extract, Transform…
CPU-based algorithm trains deep neural nets up to 15 times faster than top
Rice University computer scientists have demonstrated artificial intelligence (AI) software that runs on commodity processors and trains deep neural networks 15 times faster than platforms based on graphics ...
State of the art NLP at scale with RAPIDS, HuggingFace and Dask
See how to build end-to-end NLP pipelines in a fast and scalable way on GPUs — from feature engineering to inference.
GPU Nomenclature History: No Shortage of GPUs Here
What makes a GPU a GPU, and when did we start calling it that? Turns out that’s a more complicated question than it sounds.
The Rise, Fall and Revival of AMD (2020)
AMD is one of the oldest designers of large scale microprocessors and has been the subject of polarizing debate among technology enthusiasts for nearly 50 years. Its...
Can Graviton Win A Three-Way Compute Race At AWS?
One of the main tenets of the hyperscalers and cloud builders is that they buy what they can and they only build what they must. And if they are building
Welcome to AMD ROCm Platform — ROCm Documentation 1.0.0 documentation
AMD ROCm documentation
Using RAPIDS with PyTorch
In this post we take a look at how to use cuDF, the RAPIDS dataframe library, to do some of the preprocessing steps required to get the mortgage data in a format that PyTorch can process so that we…
Beginner’s Guide to Querying Data Using SQL on GPUs in Python
Historically speaking, processing large amounts of structured data has been the domain of relational databases. Databases, consisting of tables that can be joined together or aggregated…
Python Pandas Tutorial – Beginner’s Guide to GPU Accelerated DataFrames for
This series on the RAPIDS ecosystem explores the various aspects that enable you to solve extract, transform, load (ETL) problems, build machine learning (ML) and deep learning (DL) models…
Speculation Grows As AMD Files Patent for GPU Design
Long-time Slashdot reader UnknowingFool writes: AMD filed a patent on using chiplets for a GPU with hints on why it has waited this long to extend their CPU strategy to GPUs. The latency between chiplets poses more of a performance problem for GPUs, and AMD is attempting to…
Install the Latest Nvidia Linux Driver - LinuxConfig.org
Most of the modern Linux Desktop systems come with Nvidia driver pre-installed in a form of the Nouveau open-source graphics device driver for Nvidia video cards. Hence depending on your needs and in…
How to Install Nvidia Driver on Ubuntu 20.04
Which GPUs to get for deep learning
Here, I provide an in-depth analysis of GPUs for deep learning/machine learning and explain what is the best GPU for your use-case and budget.
Nvidia Ampere GA102 GPU Architecture [pdf]
How Micron’s GDDR6X memory is the secret to unlocking 4K on Nvidia’s RTX 30
Micron's GDDR6X is one of the star components in Nvidia's RTX 3070, 3080, and 3080 video cards. It's so fast it should boost gaming past the 4K barrier.
Diving Deep Into The Nvidia Ampere GPU Architecture
When you have 54.2 billion transistors to play with, you can pack a lot of different functionality into a computing device, and this is precisely what
NVIDIA Ampere Unleashed: NVIDIA Announces New GPU Architecture, A100 GPU, a
CUDA 11 Features Revealed | NVIDIA Developer Blog
The new NVIDIA A100 GPU based on the NVIDIA Ampere GPU architecture delivers the greatest generational leap in accelerated computing. The A100 GPU has revolutionary hardware capabilities and we’re…
Getting started with the NVIDIA Jetson Nano - PyImageSearch
In this tutorial, you will learn how to get started with your NVIDIA Jetson Nano, including installing Keras + TensorFlow, accessing the camera, and performing image classification and object detection.
Part 1 - An Overview of AMD's GPU Architectures
363 votes, 25 comments. This post has been split into a two-part series to work around Reddit’s per-post character limit. Please find Part 2 in the…
H2O.ai Releases H2O4GPU, the Fastest Collection of GPU Algorithms on the Ma
The economics of GPUs: How to train your AI model without going broke
AI won't replace you, but someone using AI will — so it’s time to embrace AI, and it’s possible to do so even on a low budget.
Memory is the Next Platform
A new crop of applications is driving the market along some unexpected routes, in some cases bypassing the processor as the landmark for performance and
$2 H100s: How the GPU Bubble Burst - by Eugene Cheah
H100s used to be $8/hr if you could get them. Now there's 7 different places sometimes selling them under $2. What happened?
Why GPU Utilization Falls Short: Understanding Streaming Multiprocessor (SM) Efficiency for Better L
Large Language Models (LLMs) have gained significant prominence in recent years, driving the need for efficient GPU utilization in machine learning tasks. However, researchers face a critical challenge in accurately assessing GPU performance. The commonly used metric, GPU…