The New HPC

By Michael Feldman

January 19, 2007

With this week's announcement of the reorganization and expansion of Tabor Communications, HPCwire and our sister publication, GRIDtoday, will begin to offer a broader view of the high performance computing and Grid domains, respectively. By recognizing that computing productivity is now the most important aspect in IT, the industry has begun to look at ways to identify it, measure it and improve it. Our new company-wide focus on High Productivity Computing means that HPCwire will be providing news and analysis of the “old” high performance computing community from an expanded perspective.

That's going to make my job a bit more complex. Unlike simple computing metrics like SPEC or Linpack benchmarks; network measurements of bandwidth and latency; or storage measurements of capacity and data transfer rates; productivity is notoriously hard to quantify. It's one of those things that people think “I know it when I see it,” but it's difficult to measure.

The economic definition of productivity is the amount of output created per unit input used. In computing systems, the outputs would be the useful calculations, but the inputs are more numerous and complex. Software development, computers, networks, external storage, energy consumption, physical infrastructure and maintenance define a whole assortment of input parameters. The interaction between all these elements creates a number of challenges. For example, a high performing computer combined with low performing external storage running an I/O-intensive application will probably waste most of its computational speed waiting for disk transfers to complete. Another part of the productivity puzzle is wrapped up in intangibles like the usability of the software development environment. So it's not enough to simply add up the costs of the individual pieces of a system.

Maybe a more useful way to think about computing productivity is as a combination of a system's performance, programmability, portability, reliability, and application workloads. And in fact these are the main criteria that were defined in DARPA's High Performance Productivity Systems (HPCS) program, which is tasked to develop the next-generation petascale computing systems. One of the main goals of this program is to develop technologies that will result in a 10X improvement in productivity. It's generally understood that this is the most important (and ambitious) goal of the program and is significantly more challenging than just achieving peak petaflops.

In the November 2006 issue of CTWatch Quarterly, which was entirely devoted to the issue of high productivity computing, authors Declan Murphy, Thomas Nash and Lawrence Votta, Jr. from Sun Microsystems and Jeremy Kepner from MIT Lincoln Laboratory described a quantitative productivity framework for high performance computing.

In the article titled “A System-wide Productivity Figure of Merit,” the authors summarize the challenge: “Establishing a single, reasonably objective and quantitative framework to compare competing high productivity computing systems has been difficult to accomplish. There are many reasons for this, not the least of which is the inevitable subjective component of the concept of productivity. Compounding the difficulty, there are many elements that make up productivity and these are weighted and interrelated differently in the wide range of contexts into which a computer may be placed.”

By starting with the relationship “productivity = utility/cost” and then decomposing utility into a number of relatively independent factors, the authors construct the framework: “In a well-balanced HPCS, significant costs will be incurred for resources other than just the CPU cycles that dominate thinking in the commodity cluster architectures. In particular, memory and bandwidth resources will have cost as much or more than CPU, and efficient programs and job allocation will have to optimize use of memory and bandwidth resources as much as CPU. Our framework allows for the inclusion of any set of significantly costly resources.”

In another article in the same CTWatch issue titled “Making the Business Case for High Performance Computing: A Benefit-Cost Analysis Methodology,” Suzy Tichenor of the Council on Competitiveness and Albert Reuther from the MIT Lincoln Laboratory developed a model that attempts to predict the return on investment (ROI) of high performance computing using a benefits-cost calculation.

Tichenor and Reuther explain: “Traditionally, HPC systems have been valued according to how fully they are utilized (i.e., the aggregate percentage of time that each of the processors of the HPC system is busy); but this valuation method treats all problems equally and does not give adequate weight to the problems that are most important to the organization. With no ability to properly assess problems having the greatest potential for driving innovation and competitive advantage, organizations risk purchasing inadequate HPC systems or, in some cases, foregoing purchases altogether because they cannot be satisfactorily justified.”

Tichenor and Reuther argue that business HPC adoption is being held back at least in part because end users focus on the costs (easy to measure) rather than the benefits (hard to measure). Certainly the economic case for more widespread use of high performance computing in the private sector would be strengthened if users had some tools to measure HPC value.

The most compelling reason to focus on productivity is to improve it. As we enter the petascale era, the gap between system peak performance and system utilization will continue to widen unless the HPC community starts to design and program these machines rather differently. With computing performance accelerating away from memory bandwidth and multi-core architectures racing ahead of application concurrency, the imbalances that already exist in our terascale systems are going to become even more severe. These escalating problems have been described from different perspectives: as a multi-core crisis, as a datacenter power/cooling crisis, and as a software crisis. But more generally, the current dilemma in high performance computing is a crisis of productivity.

End users will always be interested in the cost-effectiveness of developing, running and maintaining their applications. But this requires more than just studying some bullet points on a marketing brochure detailing gigaflops, gigabytes, and gigabits per second. By recognizing that time-to-market (or time-to-solution), total cost of ownership and ROI are functions of productivity rather than just raw hardware performance, the industry is realizing that a more sophisticated model for evaluating computing systems is going to be required.

—–

As always, comments about HPCwire are welcomed and encouraged. Write to me, Michael Feldman, at editor@hpcwire.com.

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!

Help Wanted: QED-C Survey Spotlights Skills Sought by Quantum Industry

September 28, 2021

Developing an adequate workforce for the young but fast-growing quantum information sciences industry is seen as a critical element for success. Just what that means in terms of skillsets and positions is becoming cleare Read more…

Pittsburgh Supercomputer Powers Machine Learning Analysis of Rare East Asian Stamps

September 27, 2021

Setting aside the relatively recent rise of electronic signatures, personalized stamps have been a popular form of identification for formal documents in East Asia. These identifiers – easily forged, but culturally ubi Read more…

Purdue Researchers Peer into the ‘Fog of the Machine Learning Accelerator War’

September 27, 2021

Making sense of ML performance and benchmark data is an ongoing challenge. In light of last week’s release of the most recent MLPerf (v1.1) inference results, now is perhaps a good time to review how valuable (or not) Read more…

Quantum Monte Carlo at Exascale Could Be Key to Finding New Semiconductor Materials

September 27, 2021

Researchers are urgently trying to identify possible materials to replace silicon-based semiconductors. The processing power in modern computers continues to increase even as the size of the silicon on which components a Read more…

The Case for an Edge-Driven Future for Supercomputing

September 24, 2021

“Exascale only becomes valuable when it’s creating and using data that we care about,” said Pete Beckman, co-director of the Northwestern-Argonne Institute of Science and Engineering (NAISE), at the most recent HPC Read more…

AWS Solution Channel

Introducing AWS ParallelCluster 3

Running HPC workloads, like computational fluid dynamics (CFD), molecular dynamics, or weather forecasting typically involves a lot of moving parts. You need a hundreds or thousands of compute cores, a job scheduler for keeping them fed, a shared file system that’s tuned for throughput or IOPS (or both), loads of libraries, a fast network, and a head node to make sense of all this. Read more…

Three Universities Team for NSF-Funded ‘ACES’ Reconfigurable Supercomputer Prototype

September 23, 2021

As Moore’s law slows, HPC developers are increasingly looking for speed gains in specialized code and specialized hardware – but this specialization, in turn, can make testing and deploying code trickier than ever. Now, researchers from Texas A&M University, the University of Illinois at Urbana... Read more…

Purdue Researchers Peer into the ‘Fog of the Machine Learning Accelerator War’

September 27, 2021

Making sense of ML performance and benchmark data is an ongoing challenge. In light of last week’s release of the most recent MLPerf (v1.1) inference results, Read more…

Quantum Monte Carlo at Exascale Could Be Key to Finding New Semiconductor Materials

September 27, 2021

Researchers are urgently trying to identify possible materials to replace silicon-based semiconductors. The processing power in modern computers continues to in Read more…

The Case for an Edge-Driven Future for Supercomputing

September 24, 2021

“Exascale only becomes valuable when it’s creating and using data that we care about,” said Pete Beckman, co-director of the Northwestern-Argonne Institut Read more…

Three Universities Team for NSF-Funded ‘ACES’ Reconfigurable Supercomputer Prototype

September 23, 2021

As Moore’s law slows, HPC developers are increasingly looking for speed gains in specialized code and specialized hardware – but this specialization, in turn, can make testing and deploying code trickier than ever. Now, researchers from Texas A&M University, the University of Illinois at Urbana... Read more…

Qubit Stream: Monte Carlo Advance, Infosys Joins the Fray, D-Wave Meeting Plans, and More

September 23, 2021

It seems the stream of quantum computing reports never ceases. This week – IonQ and Goldman Sachs tackle Monte Carlo on quantum hardware, Cambridge Quantum pu Read more…

Asetek Announces It Is Exiting HPC to Protect Future Profitability

September 22, 2021

Liquid cooling specialist Asetek, well-known in HPC circles for its direct-to-chip cooling technology that is inside some of the fastest supercomputers in the world, announced today that it is exiting the HPC space amid multiple supply chain issues related to the pandemic. Although pandemic supply chain... Read more…

TACC Supercomputer Delves Into Protein Interactions

September 22, 2021

Adenosine triphosphate (ATP) is a compound used to funnel energy from mitochondria to other parts of the cell, enabling energy-driven functions like muscle contractions. For ATP to flow, though, the interaction between the hexokinase-II (HKII) enzyme and the proteins found in a specific channel on the mitochondria’s outer membrane. Now, simulations conducted on supercomputers at the Texas Advanced Computing Center (TACC) have simulated... Read more…

The Latest MLPerf Inference Results: Nvidia GPUs Hold Sway but Here Come CPUs and Intel

September 22, 2021

The latest round of MLPerf inference benchmark (v 1.1) results was released today and Nvidia again dominated, sweeping the top spots in the closed (apples-to-ap Read more…

Ahead of ‘Dojo,’ Tesla Reveals Its Massive Precursor Supercomputer

June 22, 2021

In spring 2019, Tesla made cryptic reference to a project called Dojo, a “super-powerful training computer” for video data processing. Then, in summer 2020, Tesla CEO Elon Musk tweeted: “Tesla is developing a [neural network] training computer called Dojo to process truly vast amounts of video data. It’s a beast! … A truly useful exaflop at de facto FP32.” Read more…

Enter Dojo: Tesla Reveals Design for Modular Supercomputer & D1 Chip

August 20, 2021

Two months ago, Tesla revealed a massive GPU cluster that it said was “roughly the number five supercomputer in the world,” and which was just a precursor to Tesla’s real supercomputing moonshot: the long-rumored, little-detailed Dojo system. “We’ve been scaling our neural network training compute dramatically over the last few years,” said Milan Kovac, Tesla’s director of autopilot engineering. Read more…

Esperanto, Silicon in Hand, Champions the Efficiency of Its 1,092-Core RISC-V Chip

August 27, 2021

Esperanto Technologies made waves last December when it announced ET-SoC-1, a new RISC-V-based chip aimed at machine learning that packed nearly 1,100 cores onto a package small enough to fit six times over on a single PCIe card. Now, Esperanto is back, silicon in-hand and taking aim... Read more…

CentOS Replacement Rocky Linux Is Now in GA and Under Independent Control

June 21, 2021

The Rocky Enterprise Software Foundation (RESF) is announcing the general availability of Rocky Linux, release 8.4, designed as a drop-in replacement for the soon-to-be discontinued CentOS. The GA release is launching six-and-a-half months after Red Hat deprecated its support for the widely popular, free CentOS server operating system. The Rocky Linux development effort... Read more…

Intel Completes LLVM Adoption; Will End Updates to Classic C/C++ Compilers in Future

August 10, 2021

Intel reported in a blog this week that its adoption of the open source LLVM architecture for Intel’s C/C++ compiler is complete. The transition is part of In Read more…

Hot Chips: Here Come the DPUs and IPUs from Arm, Nvidia and Intel

August 25, 2021

The emergence of data processing units (DPU) and infrastructure processing units (IPU) as potentially important pieces in cloud and datacenter architectures was Read more…

AMD-Xilinx Deal Gains UK, EU Approvals — China’s Decision Still Pending

July 1, 2021

AMD’s planned acquisition of FPGA maker Xilinx is now in the hands of Chinese regulators after needed antitrust approvals for the $35 billion deal were receiv Read more…

Google Launches TPU v4 AI Chips

May 20, 2021

Google CEO Sundar Pichai spoke for only one minute and 42 seconds about the company’s latest TPU v4 Tensor Processing Units during his keynote at the Google I Read more…

Leading Solution Providers

Contributors

HPE Wins $2B GreenLake HPC-as-a-Service Deal with NSA

September 1, 2021

In the heated, oft-contentious, government IT space, HPE has won a massive $2 billion contract to provide HPC and AI services to the United States’ National Security Agency (NSA). Following on the heels of the now-canceled $10 billion JEDI contract (reissued as JWCC) and a $10 billion... Read more…

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

Quantum Roundup: IBM, Rigetti, Phasecraft, Oxford QC, China, and More

July 13, 2021

IBM yesterday announced a proof for a quantum ML algorithm. A week ago, it unveiled a new topology for its quantum processors. Last Friday, the Technical Univer Read more…

Frontier to Meet 20MW Exascale Power Target Set by DARPA in 2008

July 14, 2021

After more than a decade of planning, the United States’ first exascale computer, Frontier, is set to arrive at Oak Ridge National Laboratory (ORNL) later this year. Crossing this “1,000x” horizon required overcoming four major challenges: power demand, reliability, extreme parallelism and data movement. Read more…

Intel Launches 10nm ‘Ice Lake’ Datacenter CPU with Up to 40 Cores

April 6, 2021

The wait is over. Today Intel officially launched its 10nm datacenter CPU, the third-generation Intel Xeon Scalable processor, codenamed Ice Lake. With up to 40 Read more…

Intel Unveils New Node Names; Sapphire Rapids Is Now an ‘Intel 7’ CPU

July 27, 2021

What's a preeminent chip company to do when its process node technology lags the competition by (roughly) one generation, but outmoded naming conventions make it seem like it's two nodes behind? For Intel, the response was to change how it refers to its nodes with the aim of better reflecting its positioning within the leadership semiconductor manufacturing space. Intel revealed its new node nomenclature, and... Read more…

The Latest MLPerf Inference Results: Nvidia GPUs Hold Sway but Here Come CPUs and Intel

September 22, 2021

The latest round of MLPerf inference benchmark (v 1.1) results was released today and Nvidia again dominated, sweeping the top spots in the closed (apples-to-ap Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire