Exocompilation for Productive Programming of Hardware Accelerators
Name
Ikarashi_yuka_SM_2022_EECS_thesis.pdf
Description
Thesis PDF
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927.27 KB
Format
Adobe PDF
Checksum (MD5)
0910ef21a990bd11ef7cddbd34ab6e7e
Author(s)
Ikarashi, Yuka
Advisor(s)
Ragan-Kelley, Jonathan
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
High-performance kernel libraries are critical to exploiting accelerators and specialized instructions in many applications. Because compilers are difficult to extend to support diverse and rapidly-evolving hardware targets, and automatic optimization is often insufficient to guarantee state-of-the-art performance, these libraries are commonly still coded and optimized by hand, at great expense, in low-level C and assembly. To better support development of high-performance libraries for specialized hardware, we propose a new programming language, Exo, based on the principle of exocompilation: externalizing target-specific code generation support and optimization policies to user-level code. Exo allows custom hardware instructions, specialized memories, and accelerator configuration state to be defined in user libraries. It builds on the idea of user scheduling to externalize hardware mapping and optimization decisions. Schedules are defined as composable rewrites within the language, and we develop a set of effect analyses which guarantee program equivalence and memory safety through these transformations. We show that Exo enables rapid development of state-of-the-art matrix-matrix multiply and convolutional neural network kernels, for both an embedded neural accelerator and x86 with AVX-512 extensions, in a few dozen lines of code each.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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