Nicholas Wendt
ABSTRACT
Domain-specific languages (DSLs) improve developer productivity by abstracting away low-level details of an algorithm's implementation within a specialized domain. These languages often provide powerful primitives to describe complex operations, potentially granting flexibility during compilation to target hardware acceleration. This work proposes PriMax, a novel methodology to effectively map DSL applications to hardware accelerators. It builds decision trees based on benchmark results, which select between distinct implementations of accelerated primitives to maximize a target performance metric. In our graph analytics case study with two accelerators, PriMax produces a geometric mean speedup of 1.57x over a multicore CPU, higher than either target accelerator alone, and approaching the maximum 1.58x speedup attainable with these target accelerators.
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Index Terms
- PriMax: maximizing DSL application performance with selective primitive acceleration
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