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Publications of SPCL
|Performance Modeling for Future Computing Technologies|
(Presentation - Jun. 2018)
Invited talk at 60 years of CS @ Tsinghua celebration
AbstractWe are close to a phase-change in the computing industry. The demand for computing power is steadily increasing with the advent of (deep) learning and other high-performance computing techniques. However, the end of Dennard scaling and Moore's law force us to design for parallelism and specialization to improve compute performance. The complexity of programming is enormous and we propose the use of mathematical models to understand the performance requirements of practical algorithms. We first show pitfalls of seemingly simple performance measurements, followed by a methodology to design close-to-optimal programs. We also showcase a mathematical system design methodology for high-performance networks. All these examples testify to the value of modeling in practical high-performance computing. We assume that a broader use of these techniques and the development of a solid theory for parallel performance will lead to deep insights at many fronts.