Postcards From the Edge of Parallel Computing

By Michael Feldman

July 30, 2010

As you may have noticed, workshops, conferences, and summits on multicore and parallelism seem to be all the rage these days. A recent example is USENIX’s second annual workshop on Hot Topics in Parallelism (HotPar ’10), which took place on June 14-15 in Berkeley, California. It featured a mix of speakers from academia and industry, with perhaps a bit of a slant toward the former. The usual suspect organizations from the parallel computing universe showed up, including UC Berkeley, Intel, MIT, Stanford University, University of Illinois, Microsoft, NVIDIA, and a handful of others.

The presentations, which delved into everything from parallelizing Firefox and multicore schedulers, to the limits of GPGPU acceleration, have been conveniently posted on the workshop’s website. But if you want the Cliff Notes version, Real World Technologies covered the event, and Tarek Chammah has penned a well-constructed article highlighting some of the more interesting HotPar talks.

He also wrapped some context about the nature of parallel programming around the HotPar report. In particular, Chammah reminds us that there are different dimensions to parallelism, namely granularity, regularity, and data sharing patterns; and they are manifested in different forms. Although Chammah covers a few presentations that are a bit esoteric, such as the one on shared states in video games, the whole article is worth a read. That said, I’d like to point to two HotPar presentations that I think everyone should take a look at — and which are also covered the Real World Technologies piece.

The first is based on a paper (PDF) by Georgia Tech’s Richard Vuduc exploring the limits of GPU acceleration. Actually, I covered this topic a few weeks ago in a previous blog, so I won’t rehash it here. Suffice to say that the performance claims for GPUs and CPUs must be approached with a healthy level of skepticism. The non-controversial conclusion is that some applications are going to be better suited to one architecture or another. The unfortunate aspect to this is that one may have to do a lot of code tweaking and testing to find out which one is optimal.

The second presentation worth perusing takes a look at the parallelism “problem” from the 50,000 foot-level, specifically from the perspective of cloud computing. Karu Sankaralingam of the University of Wisconsin-Madison says that everyone needn’t get so worked up about this parallel computing thing, since it’s not the problem people think it is. In his paper, Get the Parallelism out of My Cloud (PDF), Sankaralingam argues that the nature of cloud computing, which seems poised to become the dominant computing paradigm, does not necessitate traditional rocket-science parallelism. Rather it needs concurrency (tasks running at the same time, but unrelated to one another), which is a far easier proposition, software-wise, and can be managed by a small number of software gurus that actually need to program at the level of multicore processors.

Of course, for HPC-types this is not the case. In supercomputers, true parallelism is the norm and it is pervasive. Unfortunately for Sankaralingam, he gave his cloud-parallelism soliloquy in the presence of such people, who took exception to this “Parallelism: What Me Worry” approach. From Chammah’s coverage:

…The iconoclastic talk understandably caused a minor ruckus in the room. There were no less than seven questioners lined up to challenge or pillory the speaker, who took it all in stride. It was mentioned by others that though chip throughput performance follows Moore’s Law, latency does not. Dr. [David] Patterson noted that current devices already feature multiple cores, and he bet Karu that a future iPhone will sport 8-10 application programmable cores in a few years with developers taking advantage of their data parallelism in Objective C.

Sankaralingam’s point is not without merit, though. Even in the rarified air of high performance computing, high-level languages like C and Fortran are used for most codes, with parallelism superimposed via calls to MPI and numerical libraries. Nobody programs in assembly anymore. And although the average HPC developer needs to know quite a bit more about parallel programming than the average code-slinger, more of the low-level details are probably going to end up being encapsulated in software frameworks, like Ct, Chapel, MATLAB, and so on.

Until then, the parallel programming challenge will continue to be one of the hottest topics in computer science, as well as the industry. My guess is that we will likely see a lot more HotPar workshops before this becomes a solved problem.

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

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

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

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