Daily Archives: January 5, 2021

In 2011, the movie "Contagion" eerily predicted what a future world fighting a deadly pandemic would look like. In 2020, I, along with hundreds of thousands of people around the world, saw this Hollywood prediction play out by being diagnosed with COVID-19. It was a frightening year by any measure, as every person was impacted in unique ways.

Having been involved in the development of the Internet in the 1970s, Ive seen first-hand the impact of technology on peoples lives. We are now seeing another major milestone in our lifetimethe development of a COVID-19 vaccine.

What the"Contagion" didnt show is what happens after a vaccine is developed. Now, as we enter 2021, and with the first doses of a COVID-19 vaccine being administered, a return to normal feels within reach. But what will our return to normal look like really? Here are threepredictions for 2021.

1. Continuous and episodic Internet of Medical Things monitoring devices will prove popular for remote medical diagnosis. The COVID-19 pandemic has dramatically changed the practice of clinical medicine at least in the parts of the world where Internet access is widely available and at high enough speeds to support video conferencing. A video consult is often the only choice open to patients short of going to a hospital when outpatient care is insufficient. Video-medicine is unsatisfying in the absence of good clinical data (temperature, blood pressure, pulse for example). The consequence is that health monitoring and measurement devices are increasingly valued to support remote medical diagnosis.

My Prediction: While the COVID-19 pandemic persists into 2021, demand for remote monitoring and measurement will increase. In the long run, this will lead to periodic and continuous monitoring and alerting for a wide range of chronic medical conditions. Remote medicine and early warning health prediction will in turn help citizens save on health care costs and improve and further extend life expectancy.

2. Cities will (finally) adopt self-driving cars. Self-driving cars are anything but new, having emerged from a Defense Advanced Research Projects Agency Grand Challenge in 2004. Sixteen years later, many companies are competing to make this a reality but skeptics around this technology remain.

My Prediction: In the COVID-19 aftermath, I predict driverless car service will grow in 2021 as people will opt for rides that minimize exposure to drivers and self-clean after every passenger. More cities and states will embrace driverless technology to accommodate changing transportation and public transportation preferences.

3. A practical quantum computation will be demonstrated. In 2019, Google reported that it had demonstrated an important quantum supremacy milestone by showing a computation in minutes that would have taken a conventional computer thousands of years to complete. The computation, however, did not solve any particular practical problem.

My Prediction: In the intervening period, progress has been made and it seems likely that by 2021, we will see some serious application of quantum computing to solve one or more optimization problems in mechanical design, logistics scheduling or resource allocation that would be impractical with conventional supercomputing.

Despite the challenges 2020 presented, it also unlocked some opportunities like leapfrogging with tech adoption. My hope is that the public sector sustains the speed for innovation and development to unlock even greater advancements in the year ahead.

Vinton G. Cerf is vice president and chief Internet evangelist for Google. Cerf has held positions at MCI, the Corporation for National Research Initiatives, Stanford University, UCLA and IBM. Vint Cerf served as chairman of the board of the Internet Corporation for Assigned Names and Numbers (ICANN) and was founding president of the Internet Society. He served on the U.S. National Science Board from 2013-2018.

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The Year Ahead: 3 Predictions From the 'Father of the Internet' Vint Cerf - Nextgov

ST. LOUIS, Jan. 5, 2021 /PRNewswire/ --Harris-Stowe State University announced today a multi-million dollar collaboration with IBM (NYSE: IBM)on a comprehensive program designed todevelop diverse and high demand skill sets that align with industry needs and trends so both students and faculty can develop the skills they need today for the jobs of tomorrow.

IBM and Harris-Stowe State University are building on the need to advance digital skills in education and are dedicated to providing future focused curriculum and educational tools to help train the diverse workforce of tomorrow in fast-growing technologies such as artificial intelligence (AI), blockchain, data science, cybersecurity, cloud and quantum.

"Harris-Stowe State University is thrilled to collaborate with IBM to provide greater access to skills and training in the tech industry," said Dr. Corey S. Bradford, Sr., president of Harris-Stowe State University. "As the world, more than ever relies on the use of science, technology, engineering, and mathematics to solve grand societal challenges, Harris-Stowe must continue to develop well prepared and ready graduates to join the STEM workforce. This collaboration is yet another example of our commitment to supporting student and faculty development and assisting in preparing students to compete and lead globally."

The collaboration extends IBM's recent investmentin technology, assets, resources and skills development withHBCUs across the United States through the IBM Skills Academy and enhanced IBM Academic Initiative.

"Equal access to skills and jobs is the key to unlocking economic opportunity and prosperity for diverse populations," said Valinda Scarbro Kennedy, HBCU Program Lead, IBM Global University Programs. "As we announced earlier this fall, IBM is deeply committed to helping HBCU students build their skills to better prepare for the future of work. Through this collaboration, Harris-Stowe State University students will have an opportunity to gain modern skills in emerging technologies across hybrid cloud, quantum and AI so they can be better prepared for the future of work in the digital economy."

As part of its multi-year Global University Programs, which include the IBM Academic Initiative and the IBM Skills Academy, IBM is providing more than$100M inassets,faculty training, pre-built and maintained curriculum content, hands on labs, use cases, digital badges and software to participating HBCUs. The IBM Academic Initiative provides access to resources at no-charge for teaching, learning and non-commercial research with recent enhancements including access to guest lectures. TheIBM Skills Academy is a comprehensive, integrated program through an education portal designed tocreate a foundation of diverse and high demand skill sets that directly correlate to what students will need in the workplace.The learning tracks address topics such as artificial intelligence, cybersecurity, blockchain, data science and quantum computing.

IBM's investment in HBCUs like Harris-Stowe State University is part of the company's dedicated work to promote social justice and racial equality by creating equitable, innovative experiences for HBCU students to acquire the necessary skills to help unlock economic opportunity and prosperity.

About IBMIBM is a global leader in business transformation, serving clients in more than 170 countries around the world with open hybrid cloud and AI technology. For more information, please visit here.

About Harris-Stowe State UniversityHarris-Stowe State University (HSSU), located in midtown St. Louis offers the most affordable bachelor's degree in the state of Missouri. The University is a fully accredited four-year institution with more than 50 majors, minors and certificate programs in education, business and arts and sciences. Harris-Stowe's mission is to provide outstanding educational opportunities for individuals seeking a rich and engaging academic experience. HSSU's programs are designed to nurture intellectual curiosity and build authentic skills that prepare students for leadership roles in a global society.

Media Contact:Alandrea P. Stewart, Ed.D.Harris-Stowe State UniversityO: (314) 340-3991C: (314) 203-4296[emailprotected]

SOURCE IBM

http://www.ibm.com

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IBM Provides Harris-Stowe State University with $2 Million in Artificial Intelligence and Open Hybrid Cloud Technology Resources to Help Students...

At a conference on quantum computing and finance on December 10, 2020, William Zeng, head of quantum research at Goldman Sachs, told the audience that quantum computing could have a revolutionary impact on the bank, and on finance more broadly. In a similar vein, Marco Pistoia of JP Morgan stated that new quantum machines will boost profits by speeding up asset pricing models and digging up better-performing portfolios. While there is little dispute that quantum computing has great potential to perform certain mathematical calculations much more quickly, whether it can revolutionize investing by so doing is an altogether different matter.

Get the entire 10-part series on Seth Klarman in PDF. Save it to your desktop, read it on your tablet, or email to your colleagues.

Q3 2020 hedge fund letters, conferences and more

The hope is that the immense power of quantum computers will allow investment managers earn superior investment returns by uncovering patterns in prices and financial data that can be exploited. The dark side is that quantum computers will open the door to finding patterns that either do not actually exist, or if they did exist at one time, no longer do. In more technical terms, quantum computing may allow for a new level of unwarranted data mining and lead to further confusion regarding the role of nonstationarity.

ValueWalk's Raul Panganiban interviews George Mussalli, Chief Investment Officer and Head of Equity Research at PanAgora Asset Management. In this epispode, they discuss quant ESG as well as PanAgoras unique approach to it. The following is a computer generated transcript and may contain some errors. Q3 2020 hedge fund letters, conferences and more Interview . Read More

Any actual sequence of numbers, even one generated by a random process, will have certain statistical quirks. Physicist Richard Feynman used to make this point with reference to the first 767 digits of Pi, replicated below. Allegedly (but unconfirmed) he liked to reel off the first 761 digits, and then say 9-9-9-9-9 and so on.[1] If you only look at the first 767 digits the replication of six straight nines is clearly an anomaly a potential investment opportunity. In fact, there is no discernible pattern in the digits of Pi. Feynman was purposely making fun of data mining by focusing on the first 767 digits.

3 .1 4 1 5 9 2 6 5 3 5 8 9 7 9 3 2 3 8 4 6 2 6 4 3 3 8 3 2 7 9 5 0 2 8 8 4 1 9 7 1 6 9 3 9 9 3 7 5 1 0 5 8 2 0 9 7 4 9 4 4 5 9 2 3 0 7 8 1 6 4 0 6 2 8 6 2 0 8 9 9 8 6 2 8 0 3 4 8 2 5 3 4 2 1 1 7 0 6 7 9 8 2 1 4 8 0 8 6 5 1 3 2 8 2 3 0 6 6 4 7 0 9 3 8 4 4 6 0 9 5 5 0 5 8 2 2 3 1 7 2 5 3 5 9 4 0 8 1 2 8 4 8 1 1 1 7 4 5 0 2 8 4 1 0 2 7 0 1 9 3 8 5 2 1 1 0 5 5 5 9 6 4 4 6 2 2 9 4 8 9 5 4 9 3 0 3 8 1 9 6 4 4 2 8 8 1 0 9 7 5 6 6 5 9 3 3 4 4 6 1 2 8 4 7 5 6 4 8 2 3 3 7 8 6 7 8 3 1 6 5 2 7 1 2 0 1 9 0 9 1 4 5 6 4 8 5 6 6 9 2 3 4 6 0 3 4 8 6 1 0 4 5 4 3 2 6 6 4 8 2 1 3 3 9 3 6 0 7 2 6 0 2 4 9 1 4 1 2 7 3 7 2 4 5 8 7 0 0 6 6 0 6 3 1 5 5 8 8 1 7 4 8 8 1 5 2 0 9 2 0 9 6 2 8 2 9 2 5 4 0 9 1 7 1 5 3 6 4 3 6 7 8 9 2 5 9 0 3 6 0 0 1 1 3 3 0 5 3 0 5 4 8 8 2 0 4 6 6 5 2 1 3 8 4 1 4 6 9 5 1 9 4 1 5 1 1 6 0 9 4 3 3 0 5 7 2 7 0 3 6 5 7 5 9 5 9 1 9 5 3 0 9 2 1 8 6 1 1 7 3 8 1 9 3 2 6 1 1 7 9 3 1 0 5 1 1 8 5 4 8 0 7 4 4 6 2 3 7 9 9 6 2 7 4 9 5 6 7 3 5 1 8 8 5 7 5 2 7 2 4 8 9 1 2 2 7 9 3 8 1 8 3 0 1 1 9 4 9 1 2 9 8 3 3 6 7 3 3 6 2 4 4 0 6 5 6 6 4 3 0 8 6 0 2 1 3 9 4 9 4 6 3 9 5 2 2 4 7 3 7 1 9 0 7 0 2 1 7 9 8 6 0 9 4 3 7 0 2 7 7 0 5 3 9 2 1 7 1 7 6 2 9 3 1 7 6 7 5 2 3 8 4 6 7 4 8 1 8 4 6 7 6 6 9 4 0 5 1 3 2 0 0 0 5 6 8 1 2 7 1 4 5 2 6 3 5 6 0 8 2 7 7 8 5 7 7 1 3 4 2 7 5 7 7 8 9 6 0 9 1 7 3 6 3 7 1 7 8 7 2 1 4 6 8 4 4 0 9 0 1 2 2 4 9 5 3 4 3 0 1 4 6 5 4 9 5 8 5 3 7 1 0 5 0 7 9 2 2 7 9 6 8 9 2 5 8 9 2 3 5 4 2 0 1 9 9 5 6 1 1 2 1 2 9 0 2 1 9 6 0 8 6 4 0 3 4 4 1 8 1 5 9 8 1 3 6 2 9 7 7 4 7 7 1 3 0 9 9 6 0 5 1 8 7 0 7 2 1 1 3 4 9 9 9 9 9 9

When it comes to investing, there is only one sequence of historical returns. With sufficient computing power and with repeated torturing of the data, anomalies are certain to be detected. A good example is factor investing. The publication of a highly influential paper by Professors Eugene Fama and Kenneth French identified three systematic investment factors, which started an industry focused on searching for additional factors. Research by Arnott, Harvey, Kalesnik and Linnainmaa reports that by year-end 2018 an implausibly large 400 significant factors had been discovered. One wonders how many such anomalies quantum computers might find.

Factor investing is just one example among many. Richard Roll, a leading academic financial economist with in-depth knowledge of the anomalies literature has also been an active financial manager. Based on his experience Roll stated that his money management firms attempted to make money from numerous anomalies widely documented in the academic literature but failed to make a nickel.

The simple fact is that if you have machines that can look closely enough at any historical data set, they will find anomalies. For instance, what about the anomalous sequence 0123456789 in the expansion of Pi.? That anomaly can be found beginning at digit 17,387,594,880.

The digits of Pi may be random, but they are stationary. The process that generates the first million digits is the same as the one which generates the million digits beginning at one trillion. The same is not true of investing. Consider, for example, providing a computer the sequence of daily returns on Apple stock from the day the company went public to the present. The computer could sift through the returns looking for patterns, but this is almost certainly a fruitless endeavor. The company that generated those returns is far from stationary. In 1978, Apple was run by two young entrepreneurs and had total revenues of $0.0078 billion. By 2019, the company was run by a large, experienced, management team and had revenues of $274 billion, an increase of about 35,000 times. The statistical process generating those returns is almost certainly nonstationary due to fundamental changes in the company generating them. To a lesser extent, the same is true of nearly every listed company. The market is constantly in flux and the companies are constantly evolving as consumer demands, government regulation, and technology, among other things, continually change. It is hard to imagine that even if there were past patterns in stock prices that were more than data mining, they would persist for long due to nonstationarity.

In the finance arena, computers and artificial intelligence work by using their massive data processing skills to find patterns that humans may miss. But in a nonstationary world the ultimate financial risk is that by the time they are identified those patterns will be gone. As a result, computerized trading comes to resemble a dog chasing its tail. This leads to excessive trading and ever rising costs without delivering superior results on average. Quantum computing risks simply adding fuel. Of course, there are individual cases where specific quant funds make highly impressive returns, but that too could be an example of data mining. Given the large number of firms in the money management business, the probability that a few do extraordinarily well is essentially one.

These criticisms are not meant to imply that quantum computing has no role to play in finance. For instance, it has great potential to improve the simulation analyses involved in assessing risk. The point here is that it will not be a holy grail for improving investment performance.

Despite the drawbacks associated with data mining and nonstationarity, there is one area in which the potential for quantum computing is particularly bright marketing quantitative investment strategies. Selling quantitative investment has always been an art. It involves convincing people that the investment manager knows something that will make them money, but which is too complicated to explain to them and, in some cases, too complicated for the manager to understand. Quantum computing takes that sales pitch to a whole new level because virtually no one will be able to understand how the machine decided that a particular investment strategy is attractive.

This skeptics take is that quantum computing will have little impact on what is ultimately the source of successful investing allocating capital to companies that have particularly bright prospects for developing profitable business in a highly uncertain and non-stationary world. Perhaps at some future date a computer will development the business judgment to determine whether Teslas business prospects justify its current stock price. Until then being able to comb through historical data in search of obscure patterns at ever increasing rates is more likely to produce profits through the generation of management fees rather than the enhancement of investor returns.

[1] The Feynman story has been repeated so often that the sequence of 9s starting at digit 762 is now referred to as the Feynman point in the expansion of Pi.

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Quantum Computing And Investing - ValueWalk

Here on the cusp of the new year, the catchphrase 2020 hindsight has a distinctly different feel. Good riddance, yes. But also proof of sciences power to mobilize and do good when called upon. Theres gratitude by those who came through less scathed, and, maybe more willingness to assist those who didnt.

Despite the unrelenting pandemic, high performance computing (HPC) proved itself an able member of the worldwide community of pandemic fighters. We should celebrate that, perhaps quietly since the work isnt done. HPC made a significant difference in speeding up and enabling vastly distributed research and funneling the results to those who could turn them into patient care, epidemiology guidance, and now vaccines. Remarkable really. Necessary, of course, but actually got done too. (Forget the quarreling; thats who we are.)

Across the Tabor family of publications, weve run more than 200 pandemic-related articles. I counted nearly 70 significant pieces in HPCwire. The early standing up of Fugaku at RIKEN, now comfortably astride the Top500 for a second time and by a significant margin, to participate in COVID-19 research is a good metaphor for HPCs mobilization. Many people and organizations contributed to the HPC v. pandemic effort and that continues.

Before spotlighting a few pandemic-related HPC activities and digging into a few other topics, lets do a speed-drive through the 2020 HPC/AI technology landscape.

Consolidation continued among chip players (Nvidia/Arm, AMD/Xilinx) while the AI chip newcomers (Cerebras, Habana (now Intel), SambaNova, Graphcore et. al.) were winning deals. Nvidias new A100 GPU is amazing and virtually everyone else is taking potshots for just that reason. Suddenly RISC-V looks very promising. Systems makers weathered 2020s storm with varying success while IBM seems to be winding down its HPC focus; it also plans to split/spin off its managed infrastructure services. Firing up Fugaku (notably a non-accelerated system) quickly was remarkable. The planned Frontier (ORNL) supercomputer now has the pole position in the U.S. exascale race ahead of the delayed Aurora (ANL).

The worldwide quantum computing frenzy is in full froth as the U.S. looks for constructive ways to spend its roughly $1.25 billion (U.S. Quantum Initiative) and, impressively, China just issued a demonstration of quantum supremacy. Theres a quiet revolution going on in storage and memory (just ask VAST Data). Nvidia/Mellanox introduced its line of 400 Gbs network devices while Ethernet launched its 800 Gbs spec. HPC-in-the-cloud is now a thing not a soon-to-be thing. AI is no longer an oddity but quickly infusing throughout HPC (That happened fast).

Last but not least, hyperscalers demonstrably rule the IT roost. Chipmakers used to, consistently punching above their weight (sales volume). Not so much now:

Ok then. Apologies for the many important topics omitted (e.g. exascale and leadership systems, neuromorphic tech, software tools (can oneAPI flourish?), newer fabrics, optical interconnect, etc.).

Lets start.

I want to highlight two HPC pandemic-related efforts, one current and one early on, and also single out the efforts of Oliver Peckham, HPCwires editor who leads our pandemic coverage which began in earnest with articles on March 6 (Summit Joins the Fight Against the Coronavirus) and March 13 (Global Supercomputing Is Mobilizing Against COVID-19). Actually, the very first piece Tech Conferences Are Being Canceled Due to Coronavirus, March 3 was more about interrupted technology events and we picked it up from our sister pub, Datanami which ran it on March 2. Weve since become a virtualized event world.

Heres an excerpt from the first Summit piece about modeling COVID-19s notorious spike:

Micholas Smith, a postdoctoral researcher at the University of Tennessee/ORNL Center for Molecular Biophysics (UT/ORNL CMB), used early studies and sequencing of the virus to build a virtual model of the spike protein.[A]fter being granted time on Summit through a discretionary allocation, Smith and his colleagues performed a series of molecular dynamics simulations on the protein, cycling through 8,000 compounds within a few days and analyzing how they bound to the spike protein, if at all.

Using Summit, we ranked these compounds based on a set of criteria related to how likely they were to bind to the S-protein spike, Smith said in aninterviewwith ORNL. In total, the team identified 77 candidate small-molecule compounds (such as medications) that they considered worthy of further experimentation, helping to narrow the field for medical researchers.

It took us a day or two whereas it would have taken months on a normal computer, said Jeremy Smith, director of UT/ORNL CMB and principal researcher for the study. Our results dont mean that we have found a cure or treatment for the Wuhan coronavirus. We are very hopeful, though, that our computational findings will both inform future studies and provide a framework that experimentalists will use to further investigate these compounds. Only then will we know whether any of them exhibit the characteristics needed to mitigate this virus.

The flood (and diversity) of efforts that followed was startling. Olivers advice on what to highlight catches the flavor of the challenge: You could go with something like the Fugaku vs. COVID-19 piece or the grocery store piece, maybe contrast them a bit, earliest vs. current simulations of viral particle spreador something like the LANL retrospective piece vs. the piece I just wrote up on their vaccine modeling. Think that might work for a how far weve come angle, either way.

Theres too much to cover.

Last week we ran Olivers article on LANL efforts to optimize vaccine distribution (At Los Alamos National Lab, Supercomputers Are Optimizing Vaccine Distribution). Heres a brief excerpt:

The new vaccines from Pfizer and Moderna have been deemed highly effective by the FDA; unfortunately, doses are likely to be limited for some time. As a result, many state governments are struggling to weigh difficult choices should the most exposed, like frontline workers, be vaccinated first? Or perhaps the most vulnerable, like the elderly and immunocompromised? And after them, whos next?

LANL was no stranger to this kind of analysis: earlier in the year, the lab had used supercomputer-powered tools like EpiCast to simulate virtual cities populated by individuals with demographic characteristics to model how COVID-19 would spread under different conditions. The first thing we looked at was whether it made a difference to prioritize certain populations such as healthcare workers or to just distribute the vaccine randomly,saidSara Del Valle, the LANL computational epidemiologist who is leading the labs COVID-19 modeling efforts. We learned that prioritizing healthcare workers first was more effective in reducing the number of COVID cases and deaths.

You get the idea. The well of HPC efforts to tackle and stymie COVID-19 is extremely deep. Turning unproven mRNA technology into a vaccine in record time was awe-inspiring and required many disciplines. For those unfamiliar with mRNA mechanism heres a brief CDC explanation as it relates to the new vaccines. Below are links to a few HPCwirearticles on the worldwide effort to bring HPC computational power to bear. (The last is a link to the HPCwire COVID-19 Archive which has links to all our major pandemic coverage):

COVID COVERAGE LINKS

Global Supercomputing Is Mobilizing Against COVID-19 (March 12, 2020)

Gordon Bell Special Prize Goes to Massive SARS-CoV-2 Simulations (November 19, 2020)

Supercomputer Research Leads to Human Trial of Potential COVID-19 Therapeutic Raloxifene (October 29, 2020)

AMDs Massive COVID-19 HPC Fund Adds 18 Institutions, 5 Petaflops of Power (September 14, 2020)

Supercomputer-Powered Research Uncovers Signs of Bradykinin Storm That May Explain COVID-19 Symptoms (July 28, 2020)

Researchers Use Frontera to Investigate COVID-19s Insidious Sugar Coating (June 16, 2020)

COVID-19 HPC Consortium Expands to Europe, Reports on Research Projects (May 28, 2020)

At SC20, an Expert Panel Braces for the Next Pandemic (December, 17, 2020)

Whats New in Computing vs. COVID-19: Cerebras, Nvidia, OpenMP & More (May 18, 2020)

Billion Molecules Against COVID-19 Challenge to Launch with Massive Supercomputing Support (April 22, 2020)

Pandemic Wipes Out 2020 HPC Market Growth, Flat to 12% Drop Expected (March 31, 2020)

[emailprotected]Turns Its Massive Crowdsourced Computer Network Against COVID-19 (March 16, 2020)

2020 HPCwire Awards Honor a Year of Remarkable COVID-19 Research (December, 23, 2020)

HPCWIRE COVID-19 COVERAGE ARCHIVE

Making sense of the processor world is challenging. Microprocessors are still the workhorses in mainstream computing with Intel retaining its giant market share despite AMDs encroachment. That said, the rise of heterogeneous computing and blended AI/HPC requirements has shifted focus to accelerators. Nvidias A100 GPU (54 billion transistors on 826mm2of silicon, worlds largest seven-nanometer chip) was launched this spring. Then at SC20 Nvidia announced an enhanced version of the A100, doubling its memory to 80GB; it now delivers 2TB/s of bandwidth. The A100 is an impressive piece of work.

The A100s most significant advantage, says Rick Stevens, associate lab director, Argonne National Laboratory, is its multi-instance GPU capability.

For many people the problem is achieving high occupancy, that is, being able to fill the GPU up because that depends on how much work you have to do. [By] introducing this MIG, this multi instance stuff that they have, theyre able to virtualize it. Most of the real-world performance wins are actually kind of throughput wins by using the virtualization. What weve seen isour big performance improvement is not that individual programs run much faster its that we can run up to seven parallel things on each GPU. When you add up the aggregate performance, you get these factors of three to five improvement over the V100, said Stevens.

Meanwhile, Intels XE GPU line is slowly trickling to market, mostly in card form. At SC20 Intel announced plans to make its high performance discrete GPUs available to early access developers. Notably, the new chips have been deployed at ANL and will serve as a transitional development vehicle for the future (2022) Aurora supercomputer, subbing in for the delayed IntelXE-HPC (Ponte Vecchio) GPUs that are the computational backbone of the system.

AMD, also at SC20, launched its latest GPU the MI100. AMD says it delivers 11.5 teraflops peak double-precision (FP64), 46.1 teraflops peak single-precision matrix (FP32), 23.1 teraflops peak single-precision (FP32), 184.6 teraflops peak half-precision (FP16) floating-point performance, and 92.3 peak teraflops of bfloat16 performance. HPCwire reported, AMDs MI100GPU presents a competitive alternative to Nvidias A100 GPU, rated at 9.7 teraflops of peak theoretical performance. However, the A100 is returning even higher performance than that on its FP64 Linpack runs. It will be interesting to see the specs of the GPU AMD eventually fields for use in its exascale system wins.

The stakes are high in what could become a GPU war. Today, Nvidia is the market leader in HPC.

Turning back to CPUs, which many in HPC/AI have begun to regard as the lesser of CPU/GPU pairings. Perhaps that will change with the spectacular showing of Fujitsus A64FX at the heart of Fugaku. Nvidias proposed acquisition of Arm, not a done deal yet (regulatory concerns), would likely inject fresh energy in what was already a surging Arm push into the datacenter. Of course, Nvidia has jumped into the systems business with its DGX line and presumably wants a home-grown CPU. The big mover of the last couple of years, AMDs Epyc microprocessor line, continues its steady incursion into Intel x86 territory.

Theres not been much discussion around Power10 beyond IBMs summer announcement that Power10 would offer a ~3x performance gain and ~2.6x core efficiency gain over Power9. The new executive director of OpenPOWER Foundation, James Kulina, says attracting more chipmakers to build Power devices is a top goal. Well see. RISC-V is definitely drawing interest but exactly how it fits into the processor puzzle is unclear. Esperanto unveiled a RISC-V based chip aimed at machine learning with 1,100 low-power cores based on the open-source RISC-V. Esperanto reported a goal of 4,000 cores on a single device. Europe is betting on RISC-V. However, at least near-term, RISC-V variants are seen as specialized chips.

The CPU waters are murkier than ever.

Sort of off in a land of their own are AI chip/system players. Their proliferation continues with the early movers winning important deployments. Some observers think 2021 will start sifting winners from the losers. Lets not forget that last year Intel stopped development of its newly-acquired Nervana line in favor of its even more newly-acquired Habana products. Its a high-risk, high-reward arena still.

PROCESSOR COVERAGE LINKS

Intel Xe-HP GPU Deployed for Aurora Exascale Development

Is the Nvidia A100 GPU Performance Worth a Hardware Upgrade?

LLNL, ANL and GSK Provide Early Glimpse into Cerebras AI System Performance

David Patterson Kicks Off AI Hardware Summit Championing Domain Specific Chips

Graphcores IPU Tackles Particle Physics, Showcasing Its Potential for Early Adopters

Intel Debuts Cooper Lake Xeons for 4- and 8-Socket Platforms

Intel Launches Stratix 10 NX FPGAs Targeting AI Workloads

Nvidias Ampere A100 GPU: Up to 2.5X the HPC, 20X the AI

AMD Launches Three New High-Frequency Epyc SKUs Aimed at Commercial HPC

IBM Debuts Power10; Touts New Memory Scheme, Security, and Inferencing

AMDs Road Ahead: 5nm Epyc, CPU-GPU Coupling, 20% CAGR

AI Newcomer SambaNova GAs Product Lineup and Offers New Service

Japans AIST Benchmarks Intel Optane; Cites Benefit for HPC and AI

Storage and memory dont get the attention they deserve. 3D XPoint memory (Intel and Micron), declining flash costs, and innovative software are transforming this technology segment. Hard disk drives and tape arent going away, but traditional storage management approaches such as tiering based on media type (speed/capacity/cost) are under attack. Newcomers WekaIO, VAST Data, and MemVerge are all-in on solid state, and a few leading-edge adopters (NERSC/Perlmutter) are taking the plunge. Data-intensive computing driven by the data flood and AI compute requirements (gotta keep those GPUs busy!) are big drivers.

Our storage systems typically see over an exabyte of I/O annually. Balancing this I/O intensive workload with the economics of storage means that at NERSC, we live and breathe tiering. And this is a snapshot of the storage hierarchy we have on the floor today at NERSC. Although it makes for a pretty picture, we dont have storage tiering because we want to, and in fact, Id go so far as to say its the opposite of what we and our users really want. Moving data between tiers has nothing to do with scientific discovery, said NERSC storage architect Glenn Lockwood during an SC20 panel.

To put some numbers behind this, last year we did a study that found that between 15% and 30% of that exabyte of I/O is not coming from our users jobs, but instead coming from data movement between storage tiers. That is to say that 15% to 30% of the I/O at NERSC is a complete waste of time in terms of advancing science. But even before that study, we knew that both the changing landscape of storage technology and the emerging large-scale data analysis and AI workloads arriving at NERSC required us to completely rethink our approach to tiered storage, said Lockwood.

Not surprisingly Intel and Micron (Optane/3D XPoint) are trying to accelerate the evolution. Micron released what it calls a heterogeneous-memory storage engine (HSE) designed for solid-state drives, memory-based storage and, ultimately, applications requiring persistent memory. Legacy storage engines born in the era of hard disk drives have historically failed to architecturally provide for the increased performance and reduced latency of next-generation nonvolatile media, said the company. Again, well see.

Software defined storage leveraging newer media has all the momentum at the moment with all of the established players IBM, DDN, Panasas, etc., mixing those capabilities into their product sets. WekaIO and Intel have battled it out for the top IO500 spot the last couple of years and Intels DAOS (distributed asynchronous object store) is slated for use in Aurora.

The concept of asynchronous IO is very interesting, noted Ari Berman, CEO, BioTeam research consultancy. Its essentially a queue mechanism at the system write level so system waits in the processors dont have to happen while a confirmed write back comes from the disks. So asynchronous IO allows jobs can keep running while youre waiting on storage to happen, to a limit of course. That would really improve the data input-output pipelines in those systems. Its a very interesting idea. I like asynchronous data writes and asynchronous storage access. I can see there very easily being corruption that creeps into those types of things and data without very careful sequencing. It will be interesting to watch. If it works it will be a big innovation.

Change is afoot and the storage technology community is adapting. Memory technology is also advancing.

Micron introduced a 176-layer 3D NAND flash memory at SC230 that it says increases read and write densities by more than 35 percent.JEDEC published the DDR5 SDRAM spec, the next-generation standard for random access memory (RAM) in the summer. Compared to DDR4, the DDR5 spec will deliver twice the performance and improved power efficiency, addressing ever-growing demand from datacenter and cloud environments, as well as artificial intelligence and HPC applications. At launch, DDR5 modules will reach 4.8 Gbps, providing a 50 percent improvement versus the previous generation. Density goes up four-fold with maximum density increasing from 16 Gigabits per die to 64 Gigabits per die in the new spec. JEDEC representatives indicated there will be 8 Gb and 16 Gb DDR5 products at launch.

There are always the wildcards. IBMs memristive technology is moving closer to practical use. One outlier is DNA-based storage. Dave Turek, longtime IBMer, joined DNA storage start-up Catalog this year and, says Catalog is working on proof of concepts with government agencies and a number of Fortune 500 companies. Some of these are whos-who HPC players, but some are non-HPC players many names you would recognizeWere at what I would say is the beginning of the commercial beginning. Again, well see.

STORAGE & MEMORY LINKS

SC20 Panel OK, You Hate Storage Tiering. Whats Next Then?

Intels Optane/DAOS Solution Tops Latest IO500

Startup MemVerge on Memory-centric Mission

HPC Strategist Dave Turek Joins DNA Storage (and Computing) Company Catalog

DDN-Tintri Showcases Technology Integration with Two New Products

Intel Refreshes Optane Persistent Memory, Adds New NAND SSDs

Micron Boosts Flash Density with 176-Layer 3D NAND

DDR5 Memory Spec Doubles Data Rate, Quadruples Density

IBM Touts STT MRAM Technology at IDEM 2020

The Distributed File Systems and Object Storage Landscape: Whos Leading?

Its tempting to omit quantum computing this year. Too much happened to summarize easily and the overall feel is of steady carry-on progress from 2019. There was, perhaps, a stronger pivot at least by press release count towards seeking early applications for near-term noisy intermediate scale quantum (NISQ) computers. Ion trap qubit technology got another important player in Honeywell which formally rolled out its effort and first system. Intel also stepped out from the shadows a bit in terms of showcasing its efforts. D-Wave launched a giant 5000-qubit machine (Advantage), again using a quantum annealing approach thats different from universal gate-based quantum system. IBM announced a stretch goal of achieving one million qubits!

Calling quantum computing a market is probably premature but monies are being spent. The Quantum Economic Development Consortium (QED-C) and Hyperion Research issued a forecast that projects the global quantum computing (QC) market worth an estimated $320 million in 2020 to grow 27% CAGR between 2020 and 2024. That would reach approximately $830 million by 2024. Chump change? Perhaps but real activity.

IBMs proposed Quantum Volume metric has drawn support as a broad benchmark of quantum computer performance. Honeywell promoted the 128QV score of its launch system. In December IBM reported it too had achieved a 128QV. The first QV reported by IBM was 16 in 2019 at the APS March meeting. Just what a QV of 128 means in determining practical usefulness is unclear but it is steady progress and even Intel agrees that QV is as good as any measure at the moment. DoE is also working on benchmarks, focusing a bit more on performance on given workloads.

[One] major component of benchmarking is asking what kind of resources does it take to run this or that interesting problem. Again, these are problems of interest to DoE, so basic science problems in chemistry and nuclear physics and things like that. What well do is take applications in chemistry and nuclear physics and convert them into what we consider a benchmark. We consider it a benchmark when we can distill a metric from it. So the metric could be the accuracy, the quality of the solution, or the resources required to get a given level of quality, said Raphael Pooser, PI for DoEs Quantum Testbed Pathfinder project at ORNL, during an HPCwire interview.

Next year seems likely to bring more benchmarking activity around system quality, qubit technology, and performance on specific problem sets. Several qubit technologies still vie for sway superconducting, trapped ion, optical, quantum dots, cold atoms, et al. The need to operate at near-zero (K) temps complicates everything. Google claimed achieving Quantum Supremacy last year. This year a group of China researchers also did so. The groups used different qubit technologies (superconducting v. optical) and Chinas effort tried to skirt criticisms that were lobbed at Googles effort. Frankly, both efforts were impressive. Russia reported early last year it would invest $790 million in quantum with achieving quantum supremacy as one goal.

Whats happening now is a kind of pell-mell rush among a larger and increasingly diverse quantum ecosystem (hardware, software, consultants, governments, academia). Fault tolerant quantum computing still seems distant but clever algorithms and error mitigation strategies to make productive use of NISQ systems, likely on narrow applications, look more and more promising.

Here are a few snapshots:

The persistent question is when will all of these efforts pay off and will they be as game-changing as many believe. With new money flowing into quantum, one has the sense there will be few abrupt changes in the next couple years barring untoward economic turns.

QUANTUM COVERAGE LINKS

IBMs Quantum Race to One Million Qubits

Googles Quantum Chemistry Simulation Suggests Promising Path Forward

Intel Connects the (Quantum) Dots in Accelerating Quantum Computing Effort

D-Wave Delivers 5000-qubit System; Targets Quantum Advantage

Honeywell Debuts Quantum System, Subscription Business Model, and Glimpse of Roadmap

Global QC Market Projected to Grow to More Than $800 million by 2024

ORNLs Raphael Pooser on DoEs Quantum Testbed Project

RigettiComputing Wins $8.6M DARPA Grant to Demonstrate Practical Quantum Computing

Braket: Amazons Cloud-First Quantum Environment Is Generally Available

IBM-led Webinar Tackles Quantum Developer Community Needs

Microsofts Azure Quantum Platform Now Offers Toshibas Simulated Bifurcation Machine

As always theres personnel shuffling. Lately hyperscalers have been taking HPC folks. Two long-time Intel executives, Debra Goldfarb and Bill Magro, recently left for the cloud Goldfarb to AWS as director for HPC products and strategy, and Magro to Google as CTO for HPC. Going in the other direction, John Martinis left Googles quantum development team and recently joined Australian start-up Silicon Quantum Computing. Ginny Rometty, of course, stepped down as CEO and chairman at IBM. IBMs long-time HPC exec Dave Turek left to take position with DNA storage start-up, Catalog, and last January, IBMer Brad McCredie joined AMD as corporate VP, GPU platforms.

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Farewell 2020: Bleak, Yes. But a Lot of Good Happened Too - HPCwire

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Quantum Computing Technologies Market, Share, Application Analysis, Regional Outlook, Competitive Strategies & Forecast up to 2025 - AlgosOnline

Lawyers love conflict. They thrive on it. If anyone can coexist with conflict, its a lawyer.

At least thats how most people think of lawyers. In reality, the opposite is more often true. The only people who love conflict might be candidates for the therapists couch. Most of us, especially lawyers, are averse to it.

The lawyer turned clinical psychologist, Larry Richard, has given personality assessments to over 5,000 lawyers over 20 years. As a tribe lawyers are disproportionately low in the personality traits of resilience and sociability. Resilience is the mark of emotional intelligence that allows one to accept failure, rejection and loss. Were not so good at that it turns out.

That may be, but what does that have to do with the economics of a successful legal practice or law department? It might surprise a few of us who subscribe to the zealous advocacy theory of legal practice that collaboration is more economically sustainable than exclusive competition.

Hold this thought in mind: in 2017 $10 billion in legal services revenue went from the BigLaw vault into the pockets of alternative legal service providers that are not law firms.

Why? Our conflict aversion is our greatest enemy in the Exponential Age of digital data, artificial intelligence and blockchain technologies. Doing better, faster and cheaper is the mantra of the collaborative economy. The legal business model that has worked extremely well in the competitive economy is on the verge of collapse, though that claim may seem a bit grandioseeven for a lawyer. But lets examine the evidence.

Unresolved Conflict in Workplaces is Expensive

Howatt HR Consulting provides a conflict cost calculator to gauge the cost of unresolved conflict in law firms and legal departments. I recently ran my calculator from the perspective of the most conflict-rich workplace I remember being a part of. It only cost $100,000 per year in lost productivity, absenteeism, health-care claims, turnover and other profit-destroying contributors. That is simply the impact of one person in that workplace! Howatt points out that the Canadian economy suffers a loss of over $16 billion each year due to unresolved conflict in the nations workplaces.

Its customarily calculated that the cost of an employees turnoverthrough termination or voluntary departure, then replacementcosts 120 percent of that employees annual compensation. For a $55,000-a-year paralegal, the cost of losing him or her is $66,000. Lost productivity, training and bringing a replacement to the same level of performance as a predecessor is not cheap.

At the British Legal Technology Forum 2018, Kevin Gold, a Mishcon de Reya managing partner, stated in a plenary session that the firm had calculated the costs of bringing a new young attorney to the point of return on investment; it was 250,000, or roughly $340,000.

I have listened as partners proudly describe the economic brilliance of their firms leverage model in terms such as, We have one associate make partner for every eight associates we hire. Theyre expendable. If they cant figure out how to succeed in our business model, we dont need them. There are more waiting for the empty chair. But losing seven associates to every one who makes partner is a very expensive proposition. Most associates who arent going to make partner are gone, voluntarily or otherwise, before they achieve third-year status.

According to Gold, the young lawyers at Mishcon de Reya become revenue-neutral somewhere close to their third year. Under the business model in my partner-friends firm, the firm loses about $2.5 million for every successful associate. Adjust the variables however you wish and the loss of treating associate attorneys as fungible is economically foolhardy, if not disastrous.

Similarly, the numerous accounts and studies of lateral attorney hires reflect how rarely the transition is economically beneficial for the firm. The laterally hired partner usually makes out like a bandit, but the firm often breaks even at best. More often the transaction is a loss leader. It may be worth the headlines, but the price borne by the bottom line can be less than rosy.

Of course, the law is one of the only professions that prohibits noncompete agreements with lawyers. A high-value executive can be bound by non-competes, but not lawyers. As a former firm executive committee member, we often said that a law firm is the only business that allows its inventory to walk out the door each night. If the lawyer doesnt return the next day, neither do their clients in most cases. When negotiating with a lateral attorney, the deal is usually cut on the basis of the attorneys portable business.

Whats the cause of all this lost revenue and profit? Unresolved conflict is usually the culprit. Perhaps its the associate who isnt popular enough with the firms power brokers and influencers to be worth the effort to resource, train, develop and treat as the resource Mishcon de Reya recognizes him or her to be. Or partners at odds with each over origination credits in the last compensation wars are more likely to engage in passive-aggressive behavior than have a conversation intended to reach agreement over a proper allocation of credit.

Admit it, you know its true. After 40 years of legal practice, Ive witnessed more unresolved conflict in law firms and legal departments than in prisons. Prisoners just take it outside. Lawyers demonstrate what we call Nashville Nice around these parts. You learn how to smile to their faces and then stab them in the back with a politically correct criticism in the Nashville fashion: Oh, shes a nice person, and I would never say anything bad about her, bless her heart. Thats conflict aversion.

Frankly, its more than an economic problem. Its a societal, emotional and health problem. Lawyer addiction, suicide and relational dysfunction exceed the general norm by a large margin. That, too, is an economic scourge.

The statistics cannot be questioned. Gender diversity in law school is far superior to that in law firms, legal departments, firm management committees, partnerships and the executive suite. Racial diversity doesnt even begin to reflect the population. The steady reduction in diversity as the organizational level of power and status increases is an indictment on our entire profession. What are the economic costs? The answer is simply unimaginableand totally unacceptable.

Thriving in the Collaborative Economy

We all remember the 1L experience when the most intimidating professor in our assigned classes made the recurrent sobering remark: Look to your right, look to the left . . . . Thus began our steady march into the competitive mindset of thinking like a lawyer. Unfortunately, for those of us wired that way, this culture of competition fed all our worst instincts. For others it was soul destroying. Richard, the lawyer turned clinical therapist, indicates thats the reason he became a psychologist.

While the law has perfected radical competitiveness, the rest of the business world is becoming radically collaborative. This transformative transition is due to the inevitability of digital power and pace. For a full exploration of the exponential nature of the Digital Age and its impact on commerce and culture, read The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, by Erik Brynjolfsson and Andrew McAfee. The authors brilliantly compare the attributes of the first half of the Machine Agefrom the steam engine up to 2006to the second half. The first was competitive leading to scarcity. The second, also known as the Exponential Age, is collaborative leading to abundance.

A recent visit to Silicon Valley revealed how cooperative business has become. I spoke with a software engineer working for Dell who supervises a software development team. Nothing abnormal about that. However, he manages a team whose members change every day on projects that change every day. A Dell engineer manages a team that one day might consist of developers from Microsoft, SAP, Google, Apple and others. They are working on open-source software that builds open-source softwarefor the benefit of all.

Some say attorneys could never do that. It would be unethical, wouldnt it? Ask Pfizer and the small number of law firms that won the privilege of doing Pfizers legal work. A few years ago the pharmaceutical company required its successful law firm bidders to share work product, lessons learned and mistakes made with the other Pfizer core counsel after each matter. Thats distinctly unconventionaland the hallmark of successful business models in the Exponential Age.

Many other professions have already arrived in the cooperative age of business. Preparing for a recent training program for the Vanderbilt Medical School Leadership College, I discovered Quantum Leadership: Building Better Partnerships for Sustainable Health, by Tim Porter-OGrady and Kathy Malloch. Remove the word health and replace it with law and the parallels are unmistakable. The tools of technology, artificial intelligence, blockchain, the internet of things and cryptocurrency are, or will be, changing everything. Even quantum computing has arrived, making traditional computing look like the tortoise versus the harethat is, quantum computers can calculate 100,000 times faster. As a result the old keep-it-so-no-one-else-can-get-it mindset is evaporating. Do you want to work on IBMs quantum computer, operating at 20 qubits and soon to be 50 qubits? Its free and open source. Go right ahead.

When did all this happen, you ask. Seemingly overnight, and without warning. Thats exponential. As a result no disciplinary expertise is sufficient in itself. Cognitive diversity is the fuel of innovation. Seeing a problem from the same perspective leads to the same old solutions. Seeing the same problem from multiple perspectives (gender, racial, religious, sexual orientation, disability and national origin) brings creativity to the table, and competition is inimical to its success.

What quantum leadership requires is a new form of leadership: one thats radically collaborative. The old commercial model is hierarchical, structured and highly command-and-control oriented. The new model is flat, team-based and relational.

The new commercial model is focused on accountability rather than responsibility and output rather than effort. My life as a lawyer was spent selling effort, not output. Time has been the coin of the realm in the law since 1956, when the ABA informed lawyers that time is your most valuable asset. Man, did we buy that, and so did our clientsuntil they tired of it. Now they want value, not effort.

The difference between the old commercial order and the new is stunning. Working in teams is not taught in law school. I have been teaching Legal Project Management at Vanderbilt Law School for six years. Law students routinely report that this class is the first time they have been asked to work in a team in law school unless they are joint J.D./M.B.A. candidates. Business students dont understand why law school doesnt value teamwork. Therein lies one of our greatest problems: our clients are team-based, and we dont know how to do that.

Replacing Hypercompetition with Collaboration

Lets return to the question of the missing $10 billion. How could BigLaw lose that much value in a year? Lets examine the data.

The data isnt secret. Its been building over 10 years. Its more than an aberration; its a statistical trend. The data is submitted voluntarily by the nations largest law firmsnamely, the Am Law 300on a monthly basis and reported in the Thomson Reuters Peer Monitor Index reports. Although anonymized, the data collected over the last 10 years is stunning. Law firms are losing market share steadily, relentlessly and without response.

Spend time with the data reported in the Georgetown Law Centers and Thomson Reuters Legal Executive Institutes annual Report on the State of the Legal Market. Ten years of BigLaw self-reporting reveals the following: all the data reflecting financial progress in time billed and billings realized, collected and banked in firm law treasuries is in long-term decline. There are two rising trends: rates and costs. This dangerous economic state is obvious to everyone. Nothing is being done except by a few high-flying firms that have figured out the antidote to demise.

Check out Table 15 in the Georgetown/Thomson Reuters report. The missing $10 billion went to nonlawyers and nonlaw firms such as PwC, Deloitte, Axiom, lexunited, Pangea3, LegalZoom and a growing host of alternative legal service providers doing law better, faster and cheaperand sometimes without a law license. Thats what the market wants.

The report pulls no punches this year. It states: Stop doubling down on your failing strategy! Citing the Harvard Business Review analysis by the same title, the report warns BigLaw leaders that their conflict aversion could make these hallmark firms irrelevant.

How so? Harvard and Georgetown Law cite the power of our mind-blindness in the face of economic peril. Its all about heuristics, the state of mind that partially determines how we react to stress and threat. Our worldview is only valuable in the context of how it was formed. Another way of saying it is, You cant tell a room full of millionaires their business model is broken. They cant hear it. This is not a function of intelligence but of experience. We cant know what we dont know.

Specifically, the mental heuristics that take over our cognitive capacity in times of economic peril can be summarized with startling reality in the following ways:

When combined, these mental heuristics, which reflect simply how the human brain works, can be a toxic brew of mind-blindness, obscuring paths to rescue and ways out of a dilemma of our own making.

Whats a body to do? We must overcome our conflict aversion and welcome a path to open, respectful and strategic conflict competence rather than our preferred resort to passive-aggressive behavior.

The Harvard Business Review article suggests rules to follow to achieve conflict competence:

Embracing the Cooperative Economy

Although unfamiliar to those of us steeped in a competitive model of economic success, the world has moved on and is continuing to stake out new opportunities for economic success through previously unheard-of degrees of cooperative effort.

Start small and learn as you go. Discover the power and the scope of building bridges rather than silos. As our digital world continues to explode in data and the power to process it, learn to learn from other disciplines. Make friends with a data scientist, a software engineer or a legal project manager. Learn to see from their perspectives.

And, most importantly, jump in, the waters fine.

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Collaboration is the Future - Mediate.com