Kavli Affiliate: Ke Wang
| First 5 Authors: Anjiang Wei, Allen Nie, Thiago S. F. X. Teixeira, Rohan Yadav, Wonchan Lee
| Summary:
Modern scientific discovery increasingly relies on high-performance computing
for complex modeling and simulation. A key challenge in improving parallel
program performance is efficiently mapping tasks to processors and data to
memory, a process dictated by intricate, low-level system code known as
mappers. Developing high-performance mappers demands days of manual tuning,
posing a significant barrier for domain scientists without systems expertise.
We introduce a framework that automates mapper development with generative
optimization, leveraging richer feedback beyond scalar performance metrics. Our
approach features the Agent-System Interface, which includes a Domain-Specific
Language (DSL) to abstract away the low-level complexity of system code and
define a structured search space, as well as AutoGuide, a mechanism that
interprets raw execution output into actionable feedback. Unlike traditional
reinforcement learning methods such as OpenTuner, which rely solely on scalar
feedback, our method finds superior mappers in far fewer iterations. With just
10 iterations, it outperforms OpenTuner even after 1000 iterations, achieving
3.8X faster performance. Our approach finds mappers that surpass expert-written
mappers by up to 1.34X speedup across nine benchmarks while reducing tuning
time from days to minutes.
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