1988 Gordon Bell Prize Creates Foundation for Successful HPC Career

Lawrence Berkeley National Laboratory Deputy Director Horst Simon still displays the 1988 Gordon Bell Prize he shared in. Behind him is the 2009 Gordon Bell Prize awarded to a team he was a part of. Photo by Roy Kaltschmidt, LBNL.

In the foyer of the main building at Lawrence Berkeley National Laboratory, a panel displaying the 13 Nobel Prize-winning researchers and projects associated with the lab takes pride of place. Just down the hallway, Deputy Lab Director Horst Simon has two awards displayed prominently in his office, Gordon Bell Prize certificates from 1988 and 2009.

Though not as famous as the prizes created by Alfred Nobel, the prizes endowed by Gordon Bell, who rose to fame as a computer designer for Digital Equipment Corp., are highly valued by the scientists whose scientific applications push the sustained performance of leading edge supercomputers. The awarding of each year’s prizes are a highlight of the SC conference held every November.

An industry article from the time.

Simon, along with Phong Vu, Cleve Ashcraft, Roger Grimes, John Lewis and Barry Peyton, achieved the first 1 gigaflop/s performance of a science application, running a general sparse matrix vectorization on a Cray Y-MP computer. At the time, Simon, Grimes and Lewis worked for Boeing Computing Services, Simon was based at NASA Ames, Vu worked at Cray Research, Ashcraft was at Yale and Peyton was at Oak Ridge National Laboratory. The 1988 prize was awarded at the IEEE CompCon meeting held in March 1989 in San Francisco.

While Lewis, Grimes and Ashcraft developed the code, Simon had been using the eight-processor

Y-MP with a peak performance of 1.6 gigaflop/s at NASA Ames and saw the potential. After doing a lot of fine tuning, the team was able to get the highest performance from what many considered a machine with the “old” vector processing technology.
“At the time, there was a lot of debate about how to go parallel,” Simon recalled. “One path was with the Cray that had a few very powerful processors, while others were looking to systems with hundreds of smaller processors.”

In fact, the other two Gordon Bell Prizes awarded in 1988, one for best price-performance and another for compiler parallelization, were for applications run on a 1,024-processor N-CUBE. But none of the highly parallel systems of that time could claim a theoretical peak in excess of 1 gigaflop/s.

Winners of the 1988 Gordon Bell Prize are presented with the award.

“I was absolutely elated to be a member of the winning team – I was early in my career and thought that an award for parallel performance was a great idea,” Simon said. “I was fortunate to be in a great group at Boeing and be part of a great team at NASA. The award came at a time when parallel computing was emerging as a hot topic and it was a great career boost – it established my credentials in HPC.”

He has continued to add to those credentials, including serving as one of four editors of the twice-yearly TOP500 list, which rates the performance of the world’s fastest supercomputers.
Simon said that the original motivation for parallel computers was not how to solve problems that ran on one processor faster, but to solve problems that needed more processors. And the goal was to increase the overall speed as you scaled up to bigger and bigger machines.

The idea for the annual prize grew out of a SIAM meeting in 1985, when a group tossed around the idea of having a prize recognizing the speedup of real applications on real parallel machines. Alan Karp of IBM got things rolling by offering $100 out of his own pocket as a prize. Since 2011, the winners share $10,000.

The very first prize was given to Robert Benner, John Gustafson and Gary Montry, all of Sandia National Laboratories. Simon said their work was a conceptual breakthrough that helped define the future of parallel computing. In the late 1980s there was a lot of discussion of how much speed-up one could actually obtain on parallel computer for a fixed problem, later called strong scaling. The Sandia group defined what became to be known as weak scaling: the motivation for parallel computers was not how to solve problems that ran on one processor faster, but to solve problems that needed more processors. And the goal was to increase the overall speed as you scaled up to bigger and bigger machines.

For the first few years, the prize was awarded at CompCon, a general computing conference. But in the early 1990s, Simon worked with the SC conference committee to bring the Gordon Bell Prize into the supercomputing conference and the submissions deadline was changed to coincide with that of the conference. But unlike today where the prize entries are a formal part of the tech program, they were initially relegated to a Birds-of-a-Feather session.

At SC06 in Tampa Bay, the Gordon Bell Prize submissions were incorporated into a single track of the Technical Program. Also in 2006, the ACM assumed sponsorship and the name was officially changed to the ACM Gordon Bell Prize.

Fast forward to 2009 and Simon was a member of a team led by IBM’s Dharmendra Modha that created the largest brain simulation to date on a supercomputer, with the number of neurons and synapses in the simulation exceeding those in a cat’s brain. The team, which also included Rajagopal Ananthanarayanan and Steven K. Esser, won a Gordon Bell Prize in a special category for “The Cat is Out of the Bag: Cortical Simulations with 109 Neurons, 1013 Synapses.” For the project, the IBM team members came up with the idea and Simon provided the link to the supercomputing resources. Although the team did not claim to have actually simulated a cat brain, the award generated a flurry of controversy.

But the goal of simulating brain activity is nothing new. For years, Simon said, scientists have wondered if super computers could be used to create super intelligence, which although it has occurred in a number of films, cannot be done in silico.

“But it’s an interesting challenge and if we can understand how the brain computes, it could help us design more efficient computers,” Simon said. “After all, our brain only needs about 20 watts of power to easily outperform a supercomputer drawing 20 megawatts. If we could simulate a chip with brainlike characteristics, the results could help us build a better chip.”

The simulation of chips on HPC systems is interesting, but not mainstream, Simon said, which leads him to a parting anecdote.

“Steve Jobs wanted to design a one-piece plastic casing for the Apple 2 and used a Cray for the simulation of the injection mold flow process,” Simon said. “And when Seymour Cray was building the Cray 2, he used an Apple computer.”