Tag Archives: gpu

Benchmarking NAMD on a GPU-Accelerated HPC Cluster with NVIDIA Tesla K40

Posted on October 10, 2014 by Eliot Eshelman

This is a tutorial on the usage of GPU-accelerated NAMD for molecular dynamics simulations. We make it simple to test your codes on the latest high-performance systems – you are free to use your own applications on our cluster and … Continue reading →

Running AMBER on a GPU Cluster

Posted on October 6, 2014 by Jan Smith

Welcome to our tutorial on GPU-accelerated AMBER! We make it easy to benchmark your applications and problem sets on the latest hardware. Our GPU Test Drive Cluster provides developers, scientists, academics, and anyone else interested in GPU computing with the … Continue reading →

CUB in Action – some simple examples using the CUB template library

Posted on June 18, 2014 by Justin Foley (for Microway)

In my previous post, I presented a brief introduction to the CUB library of CUDA primitives written by Duane Merrill of NVIDIA. CUB provides a set of highly-configurable software components, which include warp- and block-level kernel components as well as … Continue reading →

PCI-Express Root Complex Confusion?

Posted on May 2, 2014 by Eliot Eshelman

I’ve had several customers comment to me that it’s difficult to find someone that can speak with them intelligently about PCI-E root complex questions. And yet, it’s of vital importance when considering multi-CPU systems that have various PCI-Express devices (most … Continue reading →

Introducing CUDA UnBound (CUB)

Posted on April 14, 2014 by Justin Foley (for Microway)

CUB – a configurable C++ template library of high-performance CUDA primitives Each new generation of NVIDIA GPUs brings with it a dramatic increase in compute power and the pace of development over the past several years has been rapid. The … Continue reading →

NVIDIA Tesla K40 GPUs, the High Performance Choice for Many Applications

Posted on January 7, 2014 by Brett Newman

NVIDIA Tesla K40 is now the leading Tesla GPU for performance.  Here are some important use-cases where Tesla K40 might greatly accelerate your GPU-accelerated applications: Pick Tesla K40 for Large Data Sets GPU memory has always been at a greater … Continue reading →

NVIDIA Tesla K40 “Atlas” GPU Accelerator (Kepler GK110b) Up Close

Posted on November 18, 2013 by Eliot Eshelman

NVIDIA’s latest Tesla accelerator is without a doubt the most powerful GPU available. With almost 3,000 CUDA cores and 12GB GDDR5 memory, it wins in practically every* performance test you’ll see. As with the “Kepler” K20 GPUs, the Tesla K40 … Continue reading →

CUDA Code Migration (Fermi to Kepler Architecture) on Tesla GPUs

Posted on October 27, 2013 by Justin Foley (for Microway)

The debut of NVIDIA’s Kepler architecture in 2012 marked a significant milestone in the evolution of general-purpose GPU computing. In particular, Kepler GK110 (compute capability 3.5) brought unrivaled compute power and introduced a number of new features to enhance GPU … Continue reading →

Avoiding GPU Memory Performance Bottlenecks

Posted on September 30, 2013 by Justin McKennon (for Microway)

This post is Topic #3 (post 3) in our series Parallel Code: Maximizing your Performance Potential. Many applications contain algorithms which make use of multi-dimensional arrays (or matrices). For cases where threads need to index the higher dimensions of the … Continue reading →

GPU Shared Memory Performance Optimization

Posted on September 26, 2013 by Justin McKennon (for Microway)

This post is Topic #3 (post 2) in our series Parallel Code: Maximizing your Performance Potential. In my previous post, I provided an introduction to the various types of memory available for use in a CUDA application. Now that you’re … Continue reading →