The Week in HPC Research

By Nicole Hemsoth

February 14, 2013

The top research stories of the week have been hand-selected from major science centers, prominent journals and leading conference proceedings. Here’s another diverse set of items, including whole brain simulation; a look at High Performance Linpack; the coming GPGPU cloud paradigm; heterogenous GPU programming; and a comparison of accelerator-based servers.

Brain Simulation Project

The Human Brain Project, one of the most ambitious projects of its kind, has just been awarded half-a-million Euros over a 10-year timeframe. The European Commission funded the innovative program as part of its Future and Emerging Technologies (FET) flagship program. Led by Henry Markram, a neuroscientist at the Swiss Federal Institute of Technology in Lausanne, the project aims to reconstruct the brain piece-by-piece, using cutting-edge supercomputing resources.

According to the announcement:

As a result of this initiative, in neuroscience and neuroinformatics the brain simulation will collect and integrate experimental data, identifying and filling gaps in our knowledge. In medicine, the project’s results will facilitate better diagnosis, combined with disease and drug simulation. In computing, new techniques of interactive supercomputing, driven by the needs of brain simulation, will impact a range of industries, while devices and systems, modelled after the brain, will overcome fundamental limits on the energy-efficiency, reliability and programmability of current technologies, clearing the road for systems with brain-like intelligence.

The “Human Brain Project” is on track to become the world’s largest experimental facility for developing the most detailed model of the brain. The research will increase our understanding of how the human brain works, which has countless implications for technology and medicine, from personalized medical treatments to artificial intelligence breakthroughs.

Researchers are divided over the news. Detractors say it’s an impossible endeavor at our current stage of computational development to model the brain’s 86 billion neurons. To make it really interesting will mean capturing the brain’s actual creative potential and intelligence, otherwise it will just be a big computer.

Next >> An Investigation into High Performance Linpack

An Investigation into High Performance Linpack

A research item in the Proceedings of 2012 2nd IEEE International Conference on Parallel Distributed and Grid Computing, which took place Dec. 6-8, 2012, presents an analysis of process distribution in HPC cluster using High Performance Linpack.

The authors, a group of computer scientists from the Raja Ramanna Centre for Advanced Technology in Indore, India Computing acknowledge the fact that scientific endeavors increasingly rely on parallel programming techniques running on High Performance Computing Clusters (HPCC).

When it comes to measuring cluster performance, there are multiple factors to take into account. “Memory, interconnect bandwidth, number of cores per processor/ node and job complexity are the major parameters which affect and govern the peak computing power delivered by HPCC,” they write.

The paper describes the researchers’ experiments with High Performance Linpack (HPL). They use the benchmark to analyze the effect of job distribution among single processors versus distributed processors. They’re also investigating the effect of the system interconnect on job performance. The work centers on an InfiniBand-connected HPC cluster.

Next >> the GPGPU Cloud Paradigm

The GPGPU Cloud Paradigm

The increasing prevalence of hybrid HPC systems that use coprocessors like GPUs to improve performance has implications to HPC cloud. In a new research paper [PDF], a team of computer scientists from the College of Computer Science and Technology at Jilin University in Changchun, China, explores the idea of GPGPU cloud as a paradigm for general purpose computing. Their work appears in the February 2013 issue of the Tsinghua Science and Technology Journal.

The authors start with the premise that the “Kepler General Purpose GPU (GPGPU) architecture was developed to directly support GPU virtualization and make GPGPU cloud computing more broadly applicable by providing general purpose computing capability in the form of on-demand virtual resources.”

To test their theories, they developed a baseline GPGPU cloud system outfitted with Kepler GPUs. The system is comprised of a cloud layer, a server layer, and a GPGPU layer, and the paper further describes “the hardware features, task features, scheduling mechanism, and execution mechanism of each layer.” The work aims to uncover hardware potential while also improving task performance. In identifying the advantages to general-purpose computing on a GPGPU cloud, the authors show themselves to be on the forefront of an emerging paradigm.

Next >> Heterogeneous Computing on GPU Clusters

Heterogeneous Computing on GPU Clusters

A group of scientists from the University of Minnesota and University of Colorado Boulder have contributed to a recently-published book, GPU Solutions to Multi-scale Problems in Science and Engineering. Their chapter, titled High Throughput Heterogeneous Computing and Interactive Visualization on a Desktop Supercomputer, examines some of the computational improvements that have resulted from the GPU accelerator movement. Their test system, a “desktop supercomputer,” was constructed for less than $2,500 using commodity parts, including a Tesla C1060 card and a GeForce GTX 295 card. The GPU cluster runs on Linux, and employs CUDA, MPI and other software as needed.

The authors make some interesting observations, including the following:

MPI is used not only for distributing and/or transferring the computing loads among the GPU devices, but also for controlling the process of visualization. Several applications of heterogeneous computing have been successfully run on this desktop. Calculation of long-ranged forces in the n-body problem with fast multi-pole method can consume more than 85 % of the cycles and generate 480 GFLOPS of throughput. Mixed programming of CUDA-based C and Matlab has facilitated interactive visualization during simulations.

They explain that what sets their work apart from other published research is their use of multiple GPU devices on one desktop, employed by multiple users for various types of applications at the same time. They state that they have extended GPU acceleration from the single program multiple data paradigm to the multiple program multiple data paradigm, and claim “test runs have shown that running multiple applications on one GPU device or running one application across multiple GPU devices can be done as conveniently as on traditional CPUs.”

Next >> Accelerators Compared for Energy Efficiency

Accelerators Compared for Energy Efficiency

The entire book, GPU Solutions to Multi-scale Problems in Science and Engineering, is quite fascinating. Another chapter written by University of Houston’s Lennart Johnsson explores the energy efficiency of accelerated HPC servers.

Johnsson traces the evolution of mass market, specialized processors, including the Cell Broadband Engine (CBE) and graphics processors. She notes that GPUs, in particular, have received significant attention. The addition of hardware support for double-precision floating-point arithmetic, introduced three years ago, was key to this signification uptick in adoption, as was the recent support of Error Correcting Code.

To analyze the feasibility of deploying accelerated clusters, PRACE (the Partnership for Advanced Computing in Europe) performed a study, investigating three types of accelerators, the CBE, GPUs and ClearSpeed. The study assessed several metrics, including performance, efficiency, power efficiency for double-precision arithmetic and programmer productivity.

In this chapter, titled “Efficiency, Energy Efficiency and Programming of Accelerated HPC Servers: Highlights of PRACE Studies,” Johnsson presents and analyzes some of the results from those experiments. She observes that the “GPU performed surprisingly significantly better than the CPU on the sparse matrix-vector multiplication on which the ClearSpeed performed surprisingly poorly. For matrix-multiplication, HPL and FFT the ClearSpeed accelerator was by far the most energy efficient device.”

Next >> HPC Award Winners

Inaugural HPC Award Winners

The Department of Energy’s National Energy Research Scientific Computing Center (NERSC) unveiled the winners of their inaugural High Performance Computing (HPC) Achievement Awards. The announcement was made at the annual NERSC User Group meeting at the Lawrence Berkeley National Laboratory (Berkeley Lab).

All NERSC users, the awardees were selected for their innovative use of HPC resources to help solve major computational or humanitarian challenges. Two early career awards were also presented.

NERSC Director Sudip Dosanjh stated that “High performance computing is changing how science is being done, and facilitating breakthroughs that would have been impossible a decade ago. The 2013 NERSC Achievement Award winners highlight some of the ways this trend is expanding our fundamental understanding of science, and how we can use this knowledge to benefit humanity.”

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!

IBM, D-Wave Report Quantum Computing Advances

May 18, 2017

IBM said this week it has built and tested a pair of quantum computing processors, including a prototype of a commercial version. That progress follows an an Read more…

By George Leopold

PRACEdays 2017 Wraps Up in Barcelona

May 18, 2017

Barcelona has been absolutely lovely; the weather, the food, the people. I am, sadly, finishing my last day at PRACEdays 2017 with two sessions: an in-depth loo Read more…

By Kim McMahon

US, Europe, Japan Deepen Research Computing Partnership

May 18, 2017

On May 17, 2017, a ceremony was held during the PRACEdays 2017 conference in Barcelona to announce the memorandum of understanding (MOU) between PRACE in Europe Read more…

By Tiffany Trader

NSF, IARPA, and SRC Push into “Semiconductor Synthetic Biology” Computing

May 18, 2017

Research into how biological systems might be fashioned into computational technology has a long history with various DNA-based computing approaches explored. N Read more…

By John Russell

HPE Extreme Performance Solutions

Supercomputers Helping Researchers Predict Climate Change

Today’s weather and climate scientists are tasked with analyzing a massive tidal wave of data in order to better understand and predict significant changes affecting the climate. Read more…

DOE’s HPC4Mfg Leads to Paper Manufacturing Improvement

May 17, 2017

Papermaking ranks third behind only petroleum refining and chemical production in terms of energy consumption. Recently, simulations made possible by the U.S. D Read more…

By John Russell

PRACEdays 2017: The start of a beautiful week in Barcelona

May 17, 2017

Touching down in Barcelona on Saturday afternoon, it was warm, sunny, and oh so Spanish. I was greeted at my hotel with a glass of Cava to sip and treated to a Read more…

By Kim McMahon

NSF Issues $60M RFP for “Towards a Leadership-Class” System

May 16, 2017

In case you missed it, the National Science Foundation issued the request for proposals (RFP) for the next ‘Towards a Leadership-Class Computing Facility – Read more…

By John Russell

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

HPE Launches Servers, Services, and Collaboration at GTC

May 10, 2017

Hewlett Packard Enterprise (HPE) today launched a new liquid cooled GPU-driven Apollo platform based on SGI ICE architecture, a new collaboration with NVIDIA, a Read more…

By John Russell

IBM PowerAI Tools Aim to Ease Deep Learning Data Prep, Shorten Training 

May 10, 2017

A new set of GPU-powered AI software announced by IBM today brings automation to many of the tedious, time consuming and complex aspects of AI project on-rampin Read more…

By Doug Black

Bright Computing 8.0 Adds Azure, Expands Machine Learning Support

May 9, 2017

Bright Computing, long a prominent provider of cluster management tools for HPC, today released version 8.0 of Bright Cluster Manager and Bright OpenStack. The Read more…

By John Russell

Microsoft Azure Will Debut Pascal GPU Instances This Year

May 8, 2017

As Nvidia's GPU Technology Conference gets underway in San Jose, Calif., Microsoft today revealed plans to add Pascal-generation GPU horsepower to its Azure clo Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a ne Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is Read more…

By Tiffany Trader

Leading Solution Providers

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which w Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling Read more…

By Steve Campbell

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Eng Read more…

By Tiffany Trader

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn't made the task of parallel progr Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu's Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural networ Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, Read more…

By Tiffany Trader

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