Category Archives: Development

What Can You Do with a $15k NVIDIA Data Science Workstation? – Change Healthcare Data Science

Posted on March 4, 2020 by Brett Newman

NVIDIA’s Data Science Workstation Platform is designed to bring the power of accelerated computing to a broad set of data science workflows. Recently, we found out what happens when you lend a talented data scientist (with a serious appetite for … Continue reading →

One-shot Learning Methods Applied to Drug Discovery with DeepChem

Posted on July 26, 2017 by John Murphy

Experimental data sets for drug discovery are sometimes limited in size, due to the difficulty of gathering this type of data. Drug discovery data sets are expensive to obtain, and some are the result of clinical trials, which might not … Continue reading →

DeepChem – a Deep Learning Framework for Drug Discovery

Posted on April 28, 2017 by John Murphy

A powerful new open source deep learning framework for drug discovery is now available for public download on github. This new framework, called DeepChem, is python-based, and offers a feature-rich set of functionality for applying deep learning to problems in … Continue reading →

Deep Learning Applications in Science and Engineering

Posted on June 29, 2016 by John Murphy

Over the past decade, and particularly over the past several years, Deep learning applications have been developed for a wide range of scientific and engineering problems. For example, deep learning methods have recently increased the level of significance of the … Continue reading →

Accelerating Code with OpenACC and the NVIDIA Visual Profiler

Posted on March 14, 2016 by John Murphy

Comprised of a set of compiler directives, OpenACC was created to accelerate code using the many streaming multiprocessors (SM) present on a GPU. Similar to how OpenMP is used for accelerating code on multicore CPUs, OpenACC can accelerate code on … Continue reading →

AVX2 Optimization and Haswell-EP (Xeon E5-2600v3) CPU Features

Posted on October 3, 2014 by Brett Newman

We’re very excited to be delivering systems with the new Xeon E5-2600v3 and E5-1600v3 CPUs. If you are the type who loves microarchitecture details and compiler optimization, there’s a lot to gain. If you haven’t explored the latest techniques and … 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 →

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 →

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 →