The Week in HPC Research

By Tiffany Trader

March 7, 2013

The top research stories of the week have been hand-selected from prominent journals and leading conference proceedings. Here’s another diverse set of items, including novel methods of data race detection; a comparison of predictive laws; a review of FPGA’s promise; GPU virtualization using PCI Direct pass-through; and an analysis of the Amazon Web Services High-IO platform.

Scalable Data Race Detection

A team of researchers from Berkeley Lab and the University of California Berkeley are investigating cutting-edge programming languages for HPC. These are languages that promote hybrid parallelism and shared memory abstractions using a global address space. It’s a programming style that is especially prone to data races that are difficult to detect, and prior work in the field has demonstrated 10X-100X slowdowns for non-scientific programs.

In a recent paper, the computer scientists present what they say is “the first complete implementation of data race detection at scale for UPC programs.” UPC stands for Unified Parallel C, an extension of the C programming language developed by the HPC community for large-scale parallel machines. The implementation used by the Berkeley-based team tracks local and global memory references in the program. It employs two methods for reducing overhead 1) hierarchical function and instruction level sampling; and 2) exploiting the runtime persistence of aliasing and locality specific to Partitioned Global Address Space applications.

Experiments show that the best results are attained when both techniques are used in tandem. “When applying the optimizations in conjunction our tool finds all previously known data races in our benchmark programs with at most 50% overhead,” the researchers state. “Furthermore, while previous results illustrate the benefits of function level sampling, our experiences show that this technique does not work for scientific programs: instruction sampling or a hybrid approach is required.”

Their work is published in the Proceedings of the 18th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming.

Next >>

Predicting the Progress of Technology

A fascinating new study applies the scientific method to some of our most popular predictive models. A research team from MIT and the Santa Fe Institute compared several different approaches for predicting technological improvement – including Moore’s Law and Wright’s Law – to known cases of technological progress using past performance data from different industries.

Moore’s Law, theorized by Intel co-founder Gordon Moore in 1965, predicts that a chip’s transistor count will double every 18 months. In more general terms, it suggests that technologies advance exponentially with time. Wright’s Law was first formulated by Theodore Wright in 1936. Also called the Rule of Experience, it holds that progress increases with experience. Other alternative models were proposed by Goddard, Sinclair et al., and Nordhaus.

The study, which employed hindcasting, used a statistical model to rank the performance of the postulated laws. The comparison data came from a database on the cost and production of 62 different technologies. The expansive knowledge-base enabled researchers to test six different prediction principles against real-world data.

The results revealed that the law with the greatest accuracy was Wright’s Law, but Moore’s Law was a very close second. In fact, the laws themselves are more similar than previously realized.

“We discover a previously unobserved regularity that production tends to increase exponentially,” write the authors. “A combination of an exponential decrease in cost and an exponential increase in production would make Moore’s law and Wright’s law indistinguishable…. We show for the first time that these regularities are observed in data to such a degree that the performance of these two laws is nearly the same.”

“Our results show that technological progress is forecastable, with the square root of the logarithmic error growing linearly with the forecasting horizon at a typical rate of 2.5% per year,” they conclude.

The team includes Bela Nagy of the Santa Fe Institute, J. Doyne Farmer of the University of Oxford and the Santa Fe Institute, Quan Bui of St. John’s College in Santa Fe, NM, and Jessika E. Trancik of the Santa Fe Institute and MIT. Their findings are published in the online open-access journal PLOS ONE.

Next >>

FPGA Programming for the Masses

FPGAs (field programmable gate arrays) have been around for many years and show real potential for advancing HPC, but their popularity has been restricted because they are difficult to work with. This is the assertion of a group of researchers from the T.J. Watson Research Center. They argue that FPGAs won’t become mainstream until their various programmability challenges are addressed.

In a paper published last month in ACM Queue, the research team observes that there exists a spectrum of architectures, with general-purpose processors at one end and ASICs (application-specific integrated circuits) on the other. Architectures like PLDs (programmable logic devices), they argue, have that best-of-both-worlds potential in that they are closer to the hardware and can be reprogrammed. The most prominent PLD is in fact an FPGA.

The authors write:

FPGAs were long considered low-volume, low-density ASIC replacements. Following Moore’s law, however, FPGAs are getting denser and faster. Modern-day FPGAs can have up to 2 million logic cells, 68 Mbits of BRAM, more than 3,000 DSP slices, and up to 96 transceivers for implementing multigigabit communication channels. The latest FPGA families from Xilinx and Altera are more like an SoC (system-on-chip), mixing dual-core ARM processors with programmable logic on the same fabric. Coupled with higher device density and performance, FPGAs are quickly replacing ASICs and ASSPs (application-specific standard products) for implementing fixed function logic. Analysts expect the programmable IC (integrated circuit) market to reach the $10 billion mark by 2016.

The researchers note that “despite the advantages offered by FPGAs and their rapid growth, use of FPGA technology is restricted to a narrow segment of hardware programmers. The larger community of software programmers has stayed away from this technology, largely because of the challenges experienced by beginners trying to learn and use FPGAs.”

The rest of this excellent paper addresses the various challenges in detail and brings attention to the lack of support for device drivers, programming languages, and tools. The authors drive home the point that the community will only be able to leverage the benefits of FPGAs if the programming aspects are improved.

Next >>

GPU Virtualization using PCI Direct Pass-Through

The technical computing space has seen several trends develop over the past decade, among them are server virtualization, cloud computing and GPU computing. It’s clear that GPGPU computing has a role to play in HPC systems. Can these trends be combined? A research team from Chonbuk National University in South Korea has written a paper in the periodical Applied Mechanics and Materials, proposing exactly this. The investigate a method of GPU virtualization that exploits the GPU in a virtualized cloud computing environment.

The researchers claim their approach is different from previous work, which mostly reimplemented GPU programming APIs and virtual device drivers. Past research focused on sharing the GPU among virtual machines, which increased virtualization overhead. The paper describes an alternate method: the use of PCI direct pass-through.

“In our approach, bypassing virtual machine monitor layer with negligible overhead, the mechanism can achieve similar computation performance to bare-metal system and is transparent to the GPU programming APIs,” the authors write.

Next >>

Analysis of I/O Performance on AWS High I/O Platform

The HPC community is still exploring the potential of the cloud paradigm to discern the most suitable use cases. The pay-per-use basis of compute and storage resources is an attractive draw for researchers, but so is the illusion of limitless resources to tackle large-scale scientific workloads.

In the most recent edition of the Journal of Grid Computing, computer scientists from the Department of Electronics and Systems at the University of A Coruña in Spain evaluate the I/O storage subsystem on the Amazon EC2 platform, specifically the High I/O instance type, to determine its suitability for I/O-intensive applications. The High I/O instance type, released in July 2012, is backed by SSD and also provides high levels of CPU, memory and network performance.

The study looked at the low-level cloud storage devices available in Amazon EC2, ephemeral disks and Elastic Block Store (EBS) volumes, both on local and distributed file systems. It also assessed several I/O interfaces, notably POSIX, MPI-IO and HDF5, that are commonly employed by scientific workloads. The scalability of a representative parallel I/O code was also analyzed based on performance and cost.

As the results show, cloud storage devices have different performance characteristics and usage constraints. “Our comprehensive evaluation can help scientists to increase significantly (up to several times) the performance of I/O-intensive applications in Amazon EC2 cloud,” the researchers state. “An example of optimal configuration that can maximize I/O performance in this cloud is the use of a RAID 0 of 2 ephemeral disks, TCP with 9,000 bytes MTU, NFS async and MPI-IO on the High I/O instance type, which provides ephemeral disks backed by Solid State Drive (SSD) technology.”

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!

AI-Focused ‘Genius’ Supercomputer Installed at KU Leuven

April 24, 2018

Hewlett Packard Enterprise has deployed a new approximately half-petaflops supercomputer, named Genius, at Flemish research university KU Leuven. The system is built to run artificial intelligence (AI) workloads and, as Read more…

By Tiffany Trader

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

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…

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

AI-Focused ‘Genius’ Supercomputer Installed at KU Leuven

April 24, 2018

Hewlett Packard Enterprise has deployed a new approximately half-petaflops supercomputer, named Genius, at Flemish research university KU Leuven. The system is Read more…

By Tiffany Trader

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

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

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

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

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

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

Leading Solution Providers

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

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