The Week in HPC Research

By Tiffany Trader

April 18, 2013

We’ve scoured the journals and conference proceedings to bring you the top research stories of the week. This diverse set of items includes the remarkable mechanics of bone structure; details about UCSD’s Research CyberInfrastructure (RCI) Program; an efficient approach for Monte Carlo integration on GPUs; an implementation of the lattice Boltzmann method on GPU clusters; and a cloud computing programming model that focuses on predictable performance.

Understanding Bone’s Resilience

A marvel of evolution, bone structure is remarkably strong and resilient, thanks to a combination of collagen (a soft, flexible biomolecule) and the mineral hydroxyapatite (which provides support). The exact pairing of these two substances has long evaded researchers, until now. Recently a team of scientists from MIT revealed how the two materials combine to form a structure that is “simultaneously hard, tough and slightly flexible.”

Using a supercomputer, the researchers were able to model bone structure down to the atomic level to determine its basic building blocks. What they saw was fibers of collagen, strengthened with hydroxyapatite crystals. To determine the accuracy of their model, they compared the results with prior studies of real bone. They also carried out tests on their virtual fibers with different levels of collagen versus mineral, to assess the impact of stress and strain. The tests showed that the mineral crystals were able to withstand four times more stress than the collagen matrix.

“In this arrangement of tiny hydroxyapatite grains embedded in the collagen matrix, the two materials can each contribute the best of their properties. Hydroxyapatite takes most of the forces in the material, whereas collagen takes most of the stretching,” explained Mark Buehler, the project’s lead scientist, an associate professor of civil and environmental engineering (CEE) at MIT.

Thanks to recent advances in supercomputing, modeling work that would have taken years of compute time even a few years ago was completed in just months. The research could lead to a better understanding of brittle bone diseases like osteoporosis. The next step, according to Buehler, is to recreate bone-like materials in lab.

The findings were published this week in the journal Nature Communications.

Next >> UCSD’s RCI Program

UCSD’s Research CyberInfrastructure (RCI) Program

UC San Diego established its Research CyberInfrastructure (RCI) Program in 2009 to support the scientific research activities of its campus. Earlier this month, Richard Moore, Deputy Director of the San Diego Supercomputer Center, discussed the program’s progress at the 5th Annual University of Massachusetts and New England Area Librarian e-Science Symposium in Shrewsbury, Mass.

In his address titled “UCSD’s Research CyberInfrastructure (RCI) Program: Enabling Research Thru Shared Services,” Moore presented an overview of the work the Research CyberInfrastructure (RCI) Program is doing to support researchers at the University of California San Diego.

The integrated cyberinsfrastructure includes datacenter colocation, networking, centralized storage, data curation, research computing, as well as technical expertise. Moore says the program will:

  • Increase competitiveness of UCSD researchers.
  • Realize cost efficiencies and improve service via economies of scale and shared services.
  • Preserve UCSD’s digital intellectual property.
  • Save energy/$ and effectively use datacenter capital investments (colocation)

In order to better serve its research community, UCSD undertook a survey of the campus’s principal investigators (PIs). Moore provides a peek at some of the noteworthy findings of the soon-to-be-published report.

The interviews were undertaken with a broad sample of approximately 50 representative PIs. Asked where their data was coming from, the responses showed that about 50 percent was from campus instruments, 30 percent from simulations, 20 percent from field instruments, with roughly 15 percent resulting from other external sources. The percentages reflect the number of PIs not the amount of data and since individual PIs use multiple solutions, percentages total more than 100 percent.

A significant finding was the importance of stability and long-term planning. Responses show real interest in user adoption, but only if there is a strong commitment on the campus side that includes keeping prices down for a definite period of time. The survey also reflects the need for a high performance and sustainable storage service.

Next >> Monte Carlo Integration of GPUs

Monte Carlo Integration on GPUs

Researchers Rida Assaf and Dr. E. de Doncker from the College of Engineering and Applied Sciences at Western Michigan University (WMU) are exploring an efficient approach for Monte Carlo integration on GPUs.

As Assaf explains, Monte Carlo simulations are employed in many fields, including computer-aided design (e.g., automotive safety), finite elements (using tessellations), molecular modeling, particle physics, finance (cash flow, mortgage obligations), psychology/biometrics (e.g. analysis of taste testing), and statistics.

Their experiment employed the NVIDIA Tesla M2090 GPU card, which enables 665 gigaflops peak double-precision floating point performance, or 1,331 gigaflops peak single precision. Each card has 512 CUDA cores, 6 GB of GDDR5 memory and a memory bandwidth of 177 GB/sec (with error-correcting turned off).

The team leveraged the GPUs for several DICE functions, often used in nuclear physics for modeling the behavior of particle interactions. They found that the program achieved speedups of up to 181 compared to sequential execution, tested on different functions.

In the future, the researchers plan to take on multicore and distributed computations using the cluster at the High Performance Computational Science Laboratory (HPCS), Department of Computer Science at WMU.

Next >> LBM for GPU Clusters

LBM for GPU Clusters

The lattice Boltzmann method (LBM) holds tremendous promise for the challenging discipline of computational fluid dynamics. It reduces to a regular data parallel procedure making it a good fit for high performance computations. While there have been many efficient implementations of the lattice Boltzmann method for the GPU, there has not been as much work done with multi-GPU and GPU cluster implementations. However, GPU LBM solvers that can perform large scale simulations will be a big boon to researchers. So say a group of French researchers, who for these reasons, decided to undertake an MPI-CUDA implementation of the lattice Boltzmann method.

They’ve written a paper in the Parallel Computing journal describing an efficient LBM implementation for CUDA GPU clusters. They note that their “solver consists of a set of MPI communication routines and a CUDA kernel specifically designed to handle three-dimensional partitioning of the computation domain.” The performance and measurement work were carried out on a cluster using up to 24 GPUs. The final analysis showed that peak performance as well as weak and strong scalability are satisfactory, “both in terms of data throughput and parallelisation efficiency.”

Fig. 6. Communication phase — shape The upper part of the graph outlines the path followed by data leaving the sub-domain handled by GPU 0. For each face of the sub-domain, the out-going densities are written by the GPU to pinned buffers in host memory. The associated MPI process then copies the relevant densities into the edge buffers and sends both face and edge buffers to the corresponding MPI processes. The lower part of the graph describes the path followed by data entering the sub-domain handled by GPU 1. Once the reception of in-coming densities for faces and edges is completed, the associated MPI process copies the relevant data for each face of the sub-domain into pinned host memory buffers, which are read by the GPU during kernel execution. Source.

Next >> Cloud Programming Model

Cloud Programming for Predictable Performance

The International Journal of Grid and Distributed Computing includes an interesting study, titled “BSPCloud: A Hybrid Distributed-memory and Shared-memory Programming Model.”

A group of researchers from Shanghai University and China Telecom Corporation Ltd. write that “current programming models for cloud computing mainly focus on improving the efficiency of the cloud computing platforms but little has been done on the performance predictability of models.” In light of this, they are investigating a new programming model for cloud computing, called BSPCloud, that leverages multicore architectures while also providing predictable performance.

The team explain that “BSPCloud uses a hybrid of distributed-memory and shared-memory bulk synchronous parallel (BSP) programming model. Computing tasks are first divided into a set of coarse granularity bulks which are computed by the distributed-memory BSP model, and each coarse granularity bulk is further divided into a set of bulk threads which are computed by the shared-memory BSP model.”

The paper presents a proof-of-concept BSPCloud parallel programming library implemented in java. The researchers use the BSPCloud library on matrix multiplication, while the performance predictability and speedup are evaluated in the cloud platform. The results show the speedup and scalability of BSPCloud in different configurations.

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!

2017 Gordon Bell Prize Finalists Named

October 23, 2017

The three finalists for this year’s Gordon Bell Prize in High Performance Computing have been announced. They include two papers on projects run on China’s Sunway TaihuLight system and a third paper on 3D image recon Read more…

By John Russell

Data Vortex Users Contemplate the Future of Supercomputing

October 19, 2017

Last month (Sept. 11-12), HPC networking company Data Vortex held its inaugural users group at Pacific Northwest National Laboratory (PNNL) bringing together about 30 participants from industry, government and academia t Read more…

By Tiffany Trader

AI Self-Training Goes Forward at Google DeepMind

October 19, 2017

DeepMind, Google’s AI research organization, announced today in a blog that AlphaGo Zero, the latest evolution of AlphaGo (the first computer program to defeat a Go world champion) trained itself within three days to play Go at a superhuman level (i.e., better than any human) – and to beat the old version of AlphaGo – without leveraging human expertise, data or training. Read more…

By Doug Black

HPE Extreme Performance Solutions

Transforming Genomic Analytics with HPC-Accelerated Insights

Advancements in the field of genomics are revolutionizing our understanding of human biology, rapidly accelerating the discovery and treatment of genetic diseases, and dramatically improving human health. Read more…

Researchers Scale COSMO Climate Code to 4888 GPUs on Piz Daint

October 17, 2017

Effective global climate simulation, sorely needed to anticipate and cope with global warming, has long been computationally challenging. Two of the major obstacles are the needed resolution and prolonged time to compute Read more…

By John Russell

Data Vortex Users Contemplate the Future of Supercomputing

October 19, 2017

Last month (Sept. 11-12), HPC networking company Data Vortex held its inaugural users group at Pacific Northwest National Laboratory (PNNL) bringing together ab Read more…

By Tiffany Trader

AI Self-Training Goes Forward at Google DeepMind

October 19, 2017

DeepMind, Google’s AI research organization, announced today in a blog that AlphaGo Zero, the latest evolution of AlphaGo (the first computer program to defeat a Go world champion) trained itself within three days to play Go at a superhuman level (i.e., better than any human) – and to beat the old version of AlphaGo – without leveraging human expertise, data or training. Read more…

By Doug Black

Student Cluster Competition Coverage New Home

October 16, 2017

Hello computer sports fans! This is the first of many (many!) articles covering the world-wide phenomenon of Student Cluster Competitions. Finally, the Student Read more…

By Dan Olds

Intel Delivers 17-Qubit Quantum Chip to European Research Partner

October 10, 2017

On Tuesday, Intel delivered a 17-qubit superconducting test chip to research partner QuTech, the quantum research institute of Delft University of Technology (TU Delft) in the Netherlands. The announcement marks a major milestone in the 10-year, $50-million collaborative relationship with TU Delft and TNO, the Dutch Organization for Applied Research, to accelerate advancements in quantum computing. Read more…

By Tiffany Trader

Fujitsu Tapped to Build 37-Petaflops ABCI System for AIST

October 10, 2017

Fujitsu announced today it will build the long-planned AI Bridging Cloud Infrastructure (ABCI) which is set to become the fastest supercomputer system in Japan Read more…

By John Russell

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Intel Debuts Programmable Acceleration Card

October 5, 2017

With a view toward supporting complex, data-intensive applications, such as AI inference, video streaming analytics, database acceleration and genomics, Intel i Read more…

By Doug Black

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last w Read more…

By John Russell

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

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Leading Solution Providers

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Cray Moves to Acquire the Seagate ClusterStor Line

July 28, 2017

This week Cray announced that it is picking up Seagate's ClusterStor HPC storage array business for an undisclosed sum. "In short we're effectively transitioning the bulk of the ClusterStor product line to Cray," said CEO Peter Ungaro. Read more…

By Tiffany Trader

Intel Launches Software Tools to Ease FPGA Programming

September 5, 2017

Field Programmable Gate Arrays (FPGAs) have a reputation for being difficult to program, requiring expertise in specialty languages, like Verilog or VHDL. Easin Read more…

By Tiffany Trader

IBM Advances Web-based Quantum Programming

September 5, 2017

IBM Research is pairing its Jupyter-based Data Science Experience notebook environment with its cloud-based quantum computer, IBM Q, in hopes of encouraging a new class of entrepreneurial user to solve intractable problems that even exceed the capabilities of the best AI systems. Read more…

By Alex Woodie

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

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