NVIDIA Takes Aim at GPU Acceleration for Bioscience Applications

By Michael Feldman

January 14, 2010

NVIDIA has announced the Tesla Bio Workbench, a new program designed to bring together the computational components needed to run GPU-accelerated bioscience applications. The rationale is the same one NVIDIA’s been touting ever since it got into the high performance computing business: take advantage of the superior performance of the GPU in order to lower the entry point for HPC. In this case, they’ve assembled a GPU-centric workbench specifically designed for life science researchers and scientists.

In a nutshell, the Tesla Bio Workbench includes of an array of GPU-capable bioscience codes, a community Web site for downloading the codes and providing a forum for exchanging information, and, of course, recommendations for NVIDIA Tesla GPU -equipped workstations and clusters. The strategy is to educate the biotech community that applications and hardware are here and within the reach of more researchers than ever before.

Over the past couple of years, the application set for computational biology codes that are GPU friendly has grown tremendously, thanks mainly to CUDA ports of the CPU versions of the software. This has produced a large number of popular molecular dynamics and quantum chemistry software packages that can now be run on NVIDIA GPUs. These include such codes as AMBER, GROMACS, NAMD, TeraChem, and VMD, among others. A number of bioinformatics codes like CUDA-SW++ (Smith-Waterman), GPU-HMMER, and MUMmerGPU, are also available. All of these can be downloaded via the Tesla Bio Workbench from their respective owner sites. Many of these can be had free of charge, especially if their use is limited to academic research.

The motivation behind all this is NVIDIA’s recognition that computational biology is one of the lowest hanging fruits for GPU acceleration. Performance increases on the order of 10X to 100X compared to a CPU are fairly typical for these types of codes. This has not gone unnoticed. “The kind of momentum around GPUs in this domain has been perhaps the biggest and most organic that we’ve seen,” says Sumit Gupta, NVIDIA’s senior product manager for the Tesla group. According to him, a lot of biologists have turned to GPUs without any prodding from NVIDIA. The reason for this, he thinks, is that for many small and moderate-sized bio-research projects, the costs and complexity of high performance computing have become a true pain point.

The life sciences sector is already one of the largest markets for high performance computing. In 2008, 29 percent of the supercomputing cycles on TeraGrid were dedicated to bioscience applications, while another 19 percent were running related codes in chemistry and material sciences research. In the commercial realm, HPC demand is being driven by pharmaceutical companies and the emerging genomics industry in their quest for better drugs and treatments. Analyst firm IDC estimates the bioscience vertical is worth well over $1.5 billion to HPC vendors and expanding at a CAGR of 2.6 percent . By the way, that CAGR figure is post-recession; in 2008 IDC was forecasting a growth rate of 9.3 percent. Nevertheless, the prospects for HPC in this sector are significant.

Drug discovery, in particular, is one area where HPC promises to both lower costs and accelerate the pace of research. Today the physical synthesis of drug compounds and the subsequent testing in high-throughput drug screening is both expensive and time consuming, typically representing a five-year R&D cycle. On modern HPC systems, much of this work can be simulated with molecular dynamics and quantum chemistry codes, in essence, replacing expensive labor and material costs with cheap CPU cycles.

Or GPU cycles, as the case may be. NVIDIA’s point with the Tesla Bio Workbench is that GPUs can make computational bioscience a much less expensive proposition than ever before. Because of the data parallel computational capabilities of the modern graphics processor, for many science applications a GPU-equipped workstation can replace a small CPU cluster, while a moderate-sized GPU cluster can stand in for a high-end supercomputer. This lowers up-front hardware costs, energy use over the life of the system, and datacenter space.

For example, a small simulation of the satellite tobacco mosaic virus (STMV) virus using NAMD, a molecular dynamics code for biomolecular simulations, can be performed on a modern 16-CPU cluster based on quad-core x86 technology. But according to NVIDIA’s Gupta, a 4-GPU workstation with a CUDA-version of NAMD will outperform that cluster, and with just a fraction of the power consumption. From the individual researcher’s point of view “anything that keeps the job on the workstation is good,” says Gupta.

Of course, larger simulations require more computational muscle than a workstation can provide. But since these codes tend to scale very nicely, a GPU cluster is the natural path up. “The key to acceptance here is going to be the fact that it’s easy to simulate large molecules,” explains Gupta. “You don’t have to get time on a supercomputer, because that’s too restricting.” For a drug company, that means every researcher can have a GPU workstation for their own small experiments and can share a GPU cluster when they need to run a larger problem.

Commercial products resulting from GPU-powered computational biology have yet to appear. At this point the use of these methods for drug discovery at pharmaceutical companies is sporadic. And given the length of clinical trials that must follow the drug design and discovery process, Gupta thinks we probably won’t begin to hear of success stories for another five years or so. For NVIDIA, the immediate challenge is to convince the biotech industry that these GPU computational tools and platforms are ready now.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

New Exascale System for Earth Simulation Introduced

April 23, 2018

After four years of development, the Energy Exascale Earth System Model (E3SM) will be unveiled today and released to the broader scientific community this month. The E3SM project is supported by the Department of Energy Read more…

By Staff

RSC Reports 500Tflops, Hot Water Cooled System Deployed at JINR

April 18, 2018

RSC, developer of supercomputers and advanced HPC systems based in Russia, today reported deployment of “the world's first 100% ‘hot water’ liquid cooled supercomputer” at Joint Institute for Nuclear Research (JI Read more…

By Staff

New Device Spots Quantum Particle ‘Fingerprint’

April 18, 2018

Majorana particles have been observed by university researchers employing a device consisting of layers of magnetic insulators on a superconducting material. The advance opens the door to controlling the elusive particle Read more…

By George Leopold

HPE Extreme Performance Solutions

Hybrid HPC is Speeding Time to Insight and Revolutionizing Medicine

High performance computing (HPC) is a key driver of success in many verticals today, and health and life science industries are extensively leveraging these capabilities. Read more…

Cray Rolls Out AMD-Based CS500; More to Follow?

April 18, 2018

Cray was the latest OEM to bring AMD back into the fold with introduction today of a CS500 option based on AMD’s Epyc processor line. The move follows Cray’s introduction of an ARM-based system (XC-50) last November. Read more…

By John Russell

Cray Rolls Out AMD-Based CS500; More to Follow?

April 18, 2018

Cray was the latest OEM to bring AMD back into the fold with introduction today of a CS500 option based on AMD’s Epyc processor line. The move follows Cray’ Read more…

By John Russell

IBM: Software Ecosystem for OpenPOWER is Ready for Prime Time

April 16, 2018

With key pieces of the IBM/OpenPOWER versus Intel/x86 gambit settling into place – e.g., the arrival of Power9 chips and Power9-based systems, hyperscaler sup Read more…

By John Russell

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Cloud-Readiness and Looking Beyond Application Scaling

April 11, 2018

There are two aspects to consider when determining if an application is suitable for running in the cloud. The first, which we will discuss here under the title Read more…

By Chris Downing

Transitioning from Big Data to Discovery: Data Management as a Keystone Analytics Strategy

April 9, 2018

The past 10-15 years has seen a stark rise in the density, size, and diversity of scientific data being generated in every scientific discipline in the world. Key among the sciences has been the explosion of laboratory technologies that generate large amounts of data in life-sciences and healthcare research. Large amounts of data are now being stored in very large storage name spaces, with little to no organization and a general unease about how to approach analyzing it. Read more…

By Ari Berman, BioTeam, Inc.

IBM Expands Quantum Computing Network

April 5, 2018

IBM is positioning itself as a first mover in establishing the era of commercial quantum computing. The company believes in order for quantum to work, taming qu Read more…

By Tiffany Trader

FY18 Budget & CORAL-2 – Exascale USA Continues to Move Ahead

April 2, 2018

It was not pretty. However, despite some twists and turns, the federal government’s Fiscal Year 2018 (FY18) budget is complete and ended with some very positi Read more…

By Alex R. Larzelere

Nvidia Ups Hardware Game with 16-GPU DGX-2 Server and 18-Port NVSwitch

March 27, 2018

Nvidia unveiled a raft of new products from its annual technology conference in San Jose today, and despite not offering up a new chip architecture, there were still a few surprises in store for HPC hardware aficionados. Read more…

By Tiffany Trader

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

Leading Solution Providers

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

HPC and AI – Two Communities Same Future

January 25, 2018

According to Al Gara (Intel Fellow, Data Center Group), high performance computing and artificial intelligence will increasingly intertwine as we transition to Read more…

By Rob Farber

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Te Read more…

By John Russell

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Momentum Builds for US Exascale

January 9, 2018

2018 looks to be a great year for the U.S. exascale program. The last several months of 2017 revealed a number of important developments that help put the U.S. Read more…

By Alex R. Larzelere

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This