Back to the Future of Serial Speed?

By Bill Sembrat

October 30, 2013

For the last few decades we have had great increases in performance. Since going to “off-the-shelf components” and riding on the tails of increasing processor improvements along with ever greater number of chips and cores some have come to realize that this can’t go on forever.

We have filled up a number of ever-bigger rooms with racks and racks until we have come face to face with having to own your own power company just for the power needed to run these complexes. On another front I would have expected more concerns about the high cost of these high-end systems.

It also seems that others are raising doubts because recently there has been a lot questions, concerns, discussions, comments, articles about the problems, and enough concerns about the way forward to even provide extra funding, but few seem to looking at root issues and saying that moving forward things need to change.

We have been lucky to ride the train so far, much longer and further than those of us would have ever imaged. But, now it has become ever harder and more costly to keep the train going. So lets take a deeper look at the road traveled. About 20 years ago the HPC train switched paths from custom processors and custom systems to off-the-shelf processors and systems. It has been a good and fruitful ride.

I think it would be very wise to notice that recently most of the speed improvement has come from the parallel side. I (we) was always highly focused on the serial side. At this point maybe I should say something about myself. I was fortunate to have worked with Seymour Cray for many years. So, the we, I am referring to, is my involvement and experience with Seymour and Seymour’s machines. Seymour was never in any race and not really concerned about what someone else may be doing or not doing, but always interested in exploring and pushing serial speed on real workloads. We were mainly focused on serial speed because it kept things simple and made systems easier to use, easier to program, with less overhead and higher system efficiencies.

Few may have ever talked to Seymour about serial speed vs. parallel speed, but I can tell you that Seymour was always quite aware and disciplined himself to stay focused on serial speed improvements. He felt he could contribute more, add more value, was personally more challenging, and he very much like to work on, enjoyed working on serial improvements.

Although, he would never admit it, he also knew that he was the “king” of serial speed. Just a side comment, Seymour was also interested in exploring the far end of parallel processors and we had a running prototype parallel machine that had a design goal of 30,000,000 processors, but that is quite another story. Getting back to this topic we were really always highly focused on serial speed with the “Cray’s.” Over the last 20 odd years the current off-the-shelf path has relied on serial speed improvements but ever more increasingly on greater and greater parallel speed improvements. Parallel speed improvements has, naturally, associated with it higher overhead and power costs along with lower system efficiencies and now ever higher costs to get into the top of the list.

So to get large cost effective improvements I think that we now need to re-focus back to serial speed improvements. I believe that by addressing serial speed improvements that speed improvements of 50X+ can be achieved because we were addressing root level changes that could lead to these kinds of improvements. This quickly leads one to a startling conclusion that memory can’t keep up, does not work and becomes the big elephant in the room. So you really need to look at how memory is used and really the only way to see it is to wipe the slate clean and get rid of memory. In order to think about it you need first get rid of it and start again fresh. Very few may be up to the task of starting fresh with a blank sheet. This is a rather hard task and not as simple as one may think.

While Seymour always preferred blank paper pads with faint light blue lines and number 2 pencils, at a time, it seemed, everyone started using computers and in some cases even “Cray” Super Computers to design the “next” machine. Einstein never needed or used a computer for his theories and I would guess that Peter Higgs didn’t use one either. Giant leaps and great things seem to come from very simple root ideas. Also can-do-positive attitudes play a most, maybe the most important part, even over seemingly impossible tasks.

The memory model currently used is largely based on a 70-year-old model. Oh, if you can wake up the guys that came up with the model, that were in the farm house/barn in Princeton at the time, they would be quite amazed at the great strides and progress but in very short order they would be able to program today’s machines – so in some ways things really haven’t changed much. Other areas will need to be addressed and changed, but memory is the first and most looming problem. Because these changes are deeper root issues they should be hidden from users and even and from most of the vast layers of existing software. Funny you may think that this is new but most was tried and used years ago, but never commercialized and sometimes discarded because of lack of the-then-current available technology.

Well, yes I do believe that by addressing some deep root issues that over time large serial speed improvements can be achieved, but to use them you will quickly come to the several conclusions including that you must deal with new ways to see and use memory and all that this implies. To achieve very large improvements, I think, the focus needs to be on very several very fundamental and root changes and then apply all the parallel knowledge and improvements made over the last 20 years. Now here, I believe, may be a bigger problem. In the US we have been blessed with chip and system vendors that have been able to supply ever-increasing speeds and lots of chips and cores so we have been glued to that path but others may be unencumbered, highly motivated and more able to do something new and different.

Although they may operate under different set of rules and have additional other problems they do not have as much invested in existing ideas, enterprises, hard plant and equipment; and may be less locked in and may be more willing to change pathways. So I am concerned with our current shortsighted attitude and lack of “Americanism” in keeping the leadership local.

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!

TACC Helps ROSIE Bioscience Gateway Expand its Impact

April 26, 2017

Biomolecule structure prediction has long been challenging not least because the relevant software and workflows often require high-end HPC systems that many bioscience researchers lack easy access to. Read more…

By John Russell

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

IBM, Nvidia, Stone Ridge Claim Gas & Oil Simulation Record

April 25, 2017

IBM, Nvidia, and Stone Ridge Technology today reported setting the performance record for a “billion cell” oil and gas reservoir simulation. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Remote Visualization Optimizing Life Sciences Operations and Care Delivery

As patients continually demand a better quality of care and increasingly complex workloads challenge healthcare organizations to innovate, investing in the right technologies is key to ensuring growth and success. Read more…

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

Musk’s Latest Startup Eyes Brain-Computer Links

April 21, 2017

Elon Musk, the auto and space entrepreneur and severe critic of artificial intelligence, is forming a new venture that reportedly will seek to develop an interface between the human brain and computers. Read more…

By George Leopold

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

CERN openlab Explores New CPU/FPGA Processing Solutions

April 14, 2017

Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems. Read more…

By Linda Barney

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference phase of neural networks (NN). Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. Read more…

By Tiffany Trader

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

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

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

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

Leading Solution Providers

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

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

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

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. 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

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

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

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

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