Domain-specific languages (DSLs) promise a significant performance and portability advantage over traditional languages. DSLs are designed to be high-level and platform-independent, allowing an optimizing compiler significant leeway when targeting a particular device. Such languages are particularly popular with emerging tensor algebra workloads. However, DSLs present their own challenge: they require programmers to learn new programming languages and put in significant effort to migrate legacy code.

We present C2TACO, a synthesis tool for synthesizing TACO, a well-known tensor DSL, from C code. We develop a smart, enumerative synthesizer that uses automatically generated IO examples and source-code analysis to efficiently generate code. C2TACO is able to synthesize 95% bench marks from a tensor benchmark suite, out-performing an alternative neural machine translation technique, and demonstrates substantially higher levels of accuracy when evaluated against two state-of-the-art existing schemes, TF-Coder and ChatGPT. Our synthesized TACO programs are, by design, portable achieving significant performance improvement when evaluated on a multi-core and GPU platform.