GPU Computing II: Where the Truth Lies

By Michael Feldman

June 24, 2010

Following my blog last week about the transition to GPU computing in HPC, I ran into a couple of items that cast the subject in a somewhat different light. One was a paper written by a team of computer science researchers at Georgia Tech titled “On the Limits of GPU Acceleration” (hat tip to NERSC’s John Shalf for bringing it to my attention.) The other item surfaced as a result of an Intel presentation on the relative merits of CPU and GPU architectures for throughput computing, titled “Debunking the 100X GPU vs. CPU Myth.” I think you can guess where this is going.

Turning first to the Georgia Tech paper, authors Richard Vuduc and four colleagues set out to compare CPU and GPU performance on three typical computations in scientific computing: iterative sparse linear solvers, sparse Cholesky factorization, and the fast multipole method. If you don’t know what those are, you can look them up later. Suffice to say that they are representitive of HPC-type algorithms that are neither completely regular, like dense matrix multiplication, or completely irregular, such as graph-intensive computations.

For these codes, Vuduc and company found that a GPU was only equivalent to one or two quad-core Nehalem CPUs performance-wise. And since a single high-end GPU draws nearly as much power as two high-end x86 CPUs, from a performance-per-watt standpoint, the GPU advantage nearly disappears. They also bring up the fact that the additional cost of transfering data between the CPU and the GPU can further narrow the built-in FLOPS advantage enjoyed by the GPU. The authors sum it up thusly:

In particular, we argue that, for a moderately complex class of “irregular” computations, even well-tuned GPGPU accelerated implementations on currently available systems will deliver performance that is, roughly speaking, only comparable to well-tuned code for general-purpose multicore CPU systems, within a roughly comparable power footprint.

The GPU technology chosen was based on NVIDIA’s Tesla C1060/S1070 and GTX285 systems, so the authors do admit that the results may have been very different if they had run these code on the lastest ATI hardware or the new NVIDIA Fermi card. Also, while the researchers made an attempt to tune both the CPU and GPU codes for best performance, they may have missed some important opportunities.

Presumably the Georgia Tech research was unencumbered by commercial agendas. Support for the work came from the National Science Foundation, the Semiconductor Research Corporation, and DARPA. It is worth noting, however, that Intel was also listed as a funder. Hmmm.

Which provides an interesting segue to our second item. At the International Symposium on Computer Architecture in Saint-Malo, France, Intel presented a paper that cast a few more aspersions on the lowly graphics processor. Like the Georgia Tech researcher, the Intel folks did their own CPU vs GPU performance benchmarking, in this case, matching the Intel Core i7 960 with the NVIDIA GTX280. They used 14 different throughput computing kernels and found a mean speedup of 2.5X in favor of the GPU. The GPU did best on the GKJ kernel (collision detection), with a 14-fold performance advantage, and worst on the Sort and Solv kernels, where the CPU actually outran the GPU.

The GPU-loving folks at NVIDIA took this as good news, however, noting the 14-fold performance advantage is quite nice, thank you. In a blog post this week, NVIDIAn Andy Keane writes:

It’s a rare day in the world of technology when a company you compete with stands up at an important conference and declares that your technology is *only* up to 14 times faster than theirs. In fact in all the 26 years I’ve been in this industry, I can’t recall another time I’ve seen a company promote competitive benchmarks that are an order of magnitude slower.

Of course the 14X value was the best kernel result for the GPU, not the average. Intel’s real point was that they couldn’t produce 100-fold increases in performance on the GPU, like NVIDIA claims for some apps. NVIDIA actually freely admits that not all codes will get the two orders of magnitude increase. Keane does, however, list ten examples of real codes where users did record a 100X or better performance boost compared to a CPU implementation. He also points out that for these throughput benchmarks, Intel relied on a previous generation GPU, the GTX280, and doubted that the testers even optimized the GPU code properly — or at all.

So what does it all mean? Well, when it comes to the CPU vs. GPU performance wars, it pays to know who’s runnning the benchmarks — not only in relation to vendor loyalties, but also programming skills, software tools they used, etc. It’s also worth comparing like-to-like as far as processor generations. In this regard, I think the NVIDIA Fermi GPU should be used as sort of a ground floor for all future benchmarks. To my mind, it represents the first GPU that can really be called “general-purpose” without rolling your eyes.

It’s also important to keep in mind the effort required to port these parallel codes to their respective platforms. Skeptics are quick to point out that porting code to a GPU requires a significant up-front investment. But in his blog Keane reminds us that scaling codes on multicore CPUs is not a guaranteed path to delivering performance gains either. As a wise computer scientist once said: “All hardware sucks; all software sucks. Some just suck more than others.”

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!

Spurred by Global Ambitions, Inspur in Joint HPC Deal with DDN

January 17, 2017

Inspur, the fast-growth cloud computing and server vendor from China that has several systems on the current Top500 list, and DDN, a leader in high-end storage, have announced a joint sales and marketing agreement to produce solutions based on DDN storage platforms integrated with servers, networking, software and services from Inspur. Read more…

By Doug Black

Weekly Twitter Roundup (Jan. 12, 2017)

January 12, 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

NSF Seeks Input on Cyberinfrastructure Advances Needed

January 12, 2017

In cased you missed it, the National Science Foundation posted a “Dear Colleague Letter” (DCL) late last week seeking input on needs for the next generation of cyberinfrastructure to support science and engineering. Read more…

By John Russell

NSF Approves Bridges Phase 2 Upgrade for Broader Research Use

January 12, 2017

The recently completed phase 2 upgrade of the Bridges supercomputer at the Pittsburgh Supercomputing Center (PSC) has been approved by the National Science Foundation (NSF) making it now available for research allocations to the national scientific community, according to an announcement posted this week on the XSEDE web site. Read more…

By John Russell

HPE Extreme Performance Solutions

Remote Visualization: An Integral Technology for Upstream Oil & Gas

As the exploration and production (E&P) of natural resources evolves into an even more complex and vital task, visualization technology has become integral for the upstream oil and gas industry. Read more…

Clemson Software Optimizes Big Data Transfers

January 11, 2017

Data-intensive science is not a new phenomenon as the high-energy physics and astrophysics communities can certainly attest, but today more and more scientists are facing steep data and throughput challenges fueled by soaring data volumes and the demands of global-scale collaboration. Read more…

By Tiffany Trader

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

UberCloud Cites Progress in HPC Cloud Computing

January 10, 2017

200 HPC cloud experiments, 80 case studies, and a ton of hands-on experience gained, that’s the harvest of four years of UberCloud HPC Experiments. Read more…

By Wolfgang Gentzsch and Burak Yenier

A Conversation with Women in HPC Director Toni Collis

January 6, 2017

In this SC16 video interview, HPCwire Managing Editor Tiffany Trader sits down with Toni Collis, the director and founder of the Women in HPC (WHPC) network, to discuss the strides made since the organization’s debut in 2014. Read more…

By Tiffany Trader

Spurred by Global Ambitions, Inspur in Joint HPC Deal with DDN

January 17, 2017

Inspur, the fast-growth cloud computing and server vendor from China that has several systems on the current Top500 list, and DDN, a leader in high-end storage, have announced a joint sales and marketing agreement to produce solutions based on DDN storage platforms integrated with servers, networking, software and services from Inspur. Read more…

By Doug Black

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

UberCloud Cites Progress in HPC Cloud Computing

January 10, 2017

200 HPC cloud experiments, 80 case studies, and a ton of hands-on experience gained, that’s the harvest of four years of UberCloud HPC Experiments. Read more…

By Wolfgang Gentzsch and Burak Yenier

A Conversation with Women in HPC Director Toni Collis

January 6, 2017

In this SC16 video interview, HPCwire Managing Editor Tiffany Trader sits down with Toni Collis, the director and founder of the Women in HPC (WHPC) network, to discuss the strides made since the organization’s debut in 2014. 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

Fast Rewind: 2016 Was a Wild Ride for HPC

December 23, 2016

Some years quietly sneak by – 2016 not so much. It’s safe to say there are always forces reshaping the HPC landscape but this year’s bunch seemed like a noisy lot. Among the noisemakers: TaihuLight, DGX-1/Pascal, Dell EMC & HPE-SGI et al., KNL to market, OPA-IB chest thumping, Fujitsu-ARM, new U.S. President-elect, BREXIT, JR’s Intel Exit, Exascale (whatever that means now), NCSA@30, whither NSCI, Deep Learning mania, HPC identity crisis…You get the picture. Read more…

By John Russell

AWI Uses New Cray Cluster for Earth Sciences and Bioinformatics

December 22, 2016

The Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), headquartered in Bremerhaven, Germany, is one of the country's premier research institutes within the Helmholtz Association of German Research Centres, and is an internationally respected center of expertise for polar and marine research. In November 2015, AWI awarded Cray a contract to install a cluster supercomputer that would help the institute accelerate time to discovery. Now the effort is starting to pay off. Read more…

By Linda Barney

Addison Snell: The ‘Wild West’ of HPC Disaggregation

December 16, 2016

We caught up with Addison Snell, CEO of HPC industry watcher Intersect360, at SC16 last month, and Snell had his expected, extensive list of insights into trends driving advanced-scale technology in both the commercial and research sectors. Read more…

By Doug Black

AWS Beats Azure to K80 General Availability

September 30, 2016

Amazon Web Services has seeded its cloud with Nvidia Tesla K80 GPUs to meet the growing demand for accelerated computing across an increasingly-diverse range of workloads. The P2 instance family is a welcome addition for compute- and data-focused users who were growing frustrated with the performance limitations of Amazon's G2 instances, which are backed by three-year-old Nvidia GRID K520 graphics cards. Read more…

By Tiffany Trader

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

Vectors: How the Old Became New Again in Supercomputing

September 26, 2016

Vector instructions, once a powerful performance innovation of supercomputing in the 1970s and 1980s became an obsolete technology in the 1990s. But like the mythical phoenix bird, vector instructions have arisen from the ashes. Here is the history of a technology that went from new to old then back to new. Read more…

By Lynd Stringer

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

Container App ‘Singularity’ Eases Scientific Computing

October 20, 2016

HPC container platform Singularity is just six months out from its 1.0 release but already is making inroads across the HPC research landscape. It's in use at Lawrence Berkeley National Laboratory (LBNL), where Singularity founder Gregory Kurtzer has worked in the High Performance Computing Services (HPCS) group for 16 years. Read more…

By Tiffany Trader

Dell EMC Engineers Strategy to Democratize HPC

September 29, 2016

The freshly minted Dell EMC division of Dell Technologies is on a mission to take HPC mainstream with a strategy that hinges on engineered solutions, beginning with a focus on three industry verticals: manufacturing, research and life sciences. "Unlike traditional HPC where everybody bought parts, assembled parts and ran the workloads and did iterative engineering, we want folks to focus on time to innovation and let us worry about the infrastructure," said Jim Ganthier, senior vice president, validated solutions organization at Dell EMC Converged Platforms Solution Division. 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

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

Leading Solution Providers

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

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

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

New Genomics Pipeline Combines AWS, Local HPC, and Supercomputing

September 22, 2016

Declining DNA sequencing costs and the rush to do whole genome sequencing (WGS) of large cohort populations – think 5000 subjects now, but many more thousands soon – presents a formidable computational challenge to researchers attempting to make sense of large cohort datasets. Read more…

By John Russell

Beyond von Neumann, Neuromorphic Computing Steadily Advances

March 21, 2016

Neuromorphic computing – brain inspired computing – has long been a tantalizing goal. The human brain does with around 20 watts what supercomputers do with megawatts. And power consumption isn’t the only difference. Fundamentally, brains ‘think differently’ than the von Neumann architecture-based computers. While neuromorphic computing progress has been intriguing, it has still not proven very practical. Read more…

By John Russell

The Exascale Computing Project Awards $39.8M to 22 Projects

September 7, 2016

The Department of Energy’s Exascale Computing Project (ECP) hit an important milestone today with the announcement of its first round of funding, moving the nation closer to its goal of reaching capable exascale computing by 2023. 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

Deep Learning Paves Way for Better Diagnostics

September 19, 2016

Stanford researchers are leveraging GPU-based machines in the Amazon EC2 cloud to run deep learning workloads with the goal of improving diagnostics for a chronic eye disease, called diabetic retinopathy. The disease is a complication of diabetes that can lead to blindness if blood sugar is poorly controlled. It affects about 45 percent of diabetics and 100 million people worldwide, many in developing nations. Read more…

By Tiffany Trader

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