Crossing the frontier from sequential to parallel processing

"The Nine Emeagwali Equations Are My Discoveries in Physics" | Famous Physicists and their Equations · 9:20 · Video: Philip Emeagwali · Watch at the source

In short: In the 1980s, the most complex problems in physics were deemed nearly impossible to solve. To crack these 'grand challenges,' Philip Emeagwali argued that we had to move beyond doing one thing at a time. He proposed a leap into a new era of parallel processing that redefined the limits of supercomputing.

Philip Emeagwali describes a pivotal shift in computational history: the transition from sequential processing to parallel processing. Before this era, computers handled tasks one by one. To address the most extreme-scaled problems in computational physics—specifically extreme-scale petroleum reservoir simulation—Emeagwali argued that standard methods were insufficient. He sought to solve what the U.S. Department of Energy classified as 'grand challenges' in supercomputing during the 1980s.

His approach required a multidisciplinary expansion of knowledge. Emeagwali claimed to have extended the frontiers of modern calculus and algebra by developing nine partial differential equations and nine partial difference equations. These were designed to function across a massive architecture: 2 to the power of 16 processors married as one cohesive unit, connected by 1,048,576 bi-directional email wires. He mapped this complex architecture to a one-to-one correspondence with the edges in a sixteenth-dimensional hyperspace.

The core of this work lies in the 'Land After Sequential Processing.' While the most important laws of physics were known centuries ago and partial differential equations were established nearly 150 years ago, the ability to compute them at scale remained a bottleneck. By moving beyond the floating-point arithmetical operations that began with the first digital programmable supercomputer in 1946, Emeagwali aimed to create a new internet and a new supercomputer capable of handling the most computation-intensive problems in existence.

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