Program Lifting using Gray-Box Behavior

Collie, Bruce; O’Boyle, Michael

doi:10.1109/pact52795.2021.00012

Cited by 5 publications

(5 citation statements)

References 43 publications

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“…Early work looked at generating Excel commands from a few examples [31]. The same concept and has been used for other tasks [21,73], including generating PyTorch or TensorFlow code from tensor inputs [46,59]. TF-Coder [59] takes as input a single userprovided example to generate equivalent TensorFlow code using type constraints and bottom-up enumerative synthesis.…”

Section: Program Synthesismentioning

confidence: 99%

C2TACO: Lifting Tensor Code to TACO

Magalhães,

Woodruff,

Polgreen

et al. 2023

Proceedings of the 22nd ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences

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Domain-specific languages (DSLs) promise a significant performance and portability advantage over traditional languages. DSLs are designed to be high-level and platformindependent, 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 guided enumerative synthesizer that uses automatically generated IO examples and source-code analysis to efficiently generate dense tensor algebra code. C2TACO is able to synthesize 95% benchmarks from a tensor benchmark suite, outperforming 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.

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Section: Program Synthesismentioning

confidence: 99%

C2TACO: Lifting Tensor Code to TACO

Magalhães,

Woodruff,

Polgreen

et al. 2023

Proceedings of the 22nd ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences

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“…Example driven synthesis. The use of input/output examples to synthesize high-level code has been explored in a number of projects [32], [58], [21], [20]. It has been used to generate pytorch or tensor-flow code from tensor inputs [49], [43].…”

Section: Related Workmentioning

confidence: 99%

mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR Using Program Synthesis

Brauckmann,

Polgreen,

Grosser

et al. 2023

2023 32nd International Conference on Parallel Architectures and Compilation Techniques (PACT)

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MLIR is an emerging compiler infrastructure for modern hardware, but existing programs cannot take advantage of MLIR's high-performance compilation if they are described in lower-level general purpose languages. Consequently, to avoid programs needing to be rewritten manually, this has led to efforts to automatically raise lower-level to higher-level dialects in MLIR. However, current methods rely on manually-defined raising rules, which limit their applicability and make them challenging to maintain as MLIR dialects evolve.We present mlirSynth -a novel approach which translates programs from lower-level MLIR dialects to high-level ones without manually defined rules. Instead, it uses available dialect definitions to construct a program space and searches it effectively using type constraints and equivalences. We demonstrate its effectiveness by raising C programs to two distinct high-level MLIR dialects, which enables us to use existing high-level dialect specific compilation flows. On Polybench, we show a greater coverage than previous approaches, resulting in geomean speedups of 2.5x (Intel) and 3.4x (AMD) over state-of-the-art compilation flows. mlirSynth also enables retargetability to domain-specific accelerators, resulting in a geomean speedup of 21.6x on a TPU.

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“…Constraint matching [27,45,57] provides a way of matching and extracting interfaces from high-level code. Unfortunately, these approaches are brittle [45] -they do not scale beyond a single implementation/accelerator pair, and constraint patterns are extremely hard to write [39,40,58]. Rewrite-rule based compilers can be used to target accelerators [77], but these still rely on initial matching using constraints or similar.…”

Section: Existing Compilation Techniquesmentioning

confidence: 99%

Bind the gap: compiling real software to hardware FFT accelerators

Woodruff

Armengol-Estapé

Ainsworth

et al. 2022

Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation

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Specialized hardware accelerators continue to be a source of performance improvement. However, such specialization comes at a programming price. The fundamental issue is that of a mismatch between the diversity of user code and the functionality of fixed hardware, limiting its wider uptake.Here we focus on a particular set of accelerators: those for Fast Fourier Transforms. We present FACC (Fourier ACcelerator Compiler), a novel approach to automatically map legacy code to Fourier Transform accelerators. It automatically generates drop-in replacement adapters using Input-Output (IO)-based program synthesis that bridge the gap between user code and accelerators. We apply FACC to unmodified GitHub C programs of varying complexity and compare against two existing approaches. We target FACC to a high-performance library, FFTW, and two hardware accelerators, the NXP PowerQuad and the Analog Devices FFTA, and demonstrate mean speedups of 9x, 17x and 27x respectively.

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Program Lifting using Gray-Box Behavior

Cited by 5 publications

References 43 publications

C2TACO: Lifting Tensor Code to TACO

C2TACO: Lifting Tensor Code to TACO

mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR Using Program Synthesis

Bind the gap: compiling real software to hardware FFT accelerators

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