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!

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

Weekly Twitter Roundup (Feb. 23, 2017)

February 23, 2017

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

HPE Server Shows Low Latency on STAC-N1 Test

February 22, 2017

The performance of trade and match servers can be a critical differentiator for financial trading houses. Read more…

By John Russell

HPC Financial Update (Feb. 2017)

February 22, 2017

In this recurring feature, we’ll provide you with financial highlights from companies in the HPC industry. Check back in regularly for an updated list with the most pertinent fiscal information. Read more…

By Thomas Ayres

HPE Extreme Performance Solutions

Manufacturers Reaping the Benefits of Remote Visualization

Today’s manufacturers are operating in an ever-changing atmosphere, and finding new ways to boost productivity has never been more vital.

This is why manufacturers are ramping up their investments in high performance computing (HPC), a trend which has helped give rise to the “connected factory” and Industrial Internet of Things (IIoT) concepts that are proliferating throughout the industry today. Read more…

Rethinking HPC Platforms for ‘Second Gen’ Applications

February 22, 2017

Just what constitutes HPC and how best to support it is a keen topic currently. Read more…

By John Russell

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 network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

ExxonMobil, NCSA, Cray Scale Reservoir Simulation to 700,000+ Processors

February 17, 2017

In a scaling breakthrough for oil and gas discovery, ExxonMobil geoscientists report they have harnessed the power of 717,000 processors – the equivalent of 22,000 32-processor computers – to run complex oil and gas reservoir simulation models. Read more…

By Doug Black

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

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 network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

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 new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

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 will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

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 was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

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 offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

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 will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

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 programming any easier. Read more…

By Tiffany Trader

Leading Solution Providers

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. 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 new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. 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 was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

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