R-Stream Compiler

Praun, Christoph von; Fineman, Jeremy T.; Leiserson, Charles E.; Gallopoulos, Efstratios; Snir, Marc; Heath, Michael R.; Grice, Don; White, Andrew G.; López, Pedro; Meister, Benoit; Vasilache, Nicolas; Wohlford, David; Baskaran, Muthu Manikandan; Leung, Allen; Lethin, Richard; Saltz, Joel; Das, Raja

doi:10.1007/978-0-387-09766-4_515

Cited by 9 publications

(4 citation statements)

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“…Polylib 5.22.5 provides macros that allow users to change the element type of the polylib data structures at compile time and it also has a hand-crafted exception system that Polylib uses to track overflows and free data structures in case an exception occurs, but Polylib does neither use SIMDization nor can it automatically transition from low-precision to higher-precision types. The R-stream compiler [Meister et al 2011] provides Jolylib, a polyhedral constraint library implemented in Java, but to our understanding, the details of this library have not been publicly discussed. The integer set library (isl) [Verdoolaege 2010] is today the state-of-the-art library for Presburger arithmetic and provides support for full Presburger arithmetic, including existential constraints.…”

Section: Related Workmentioning

confidence: 99%

FPL: fast Presburger arithmetic through transprecision

Pitchanathan

Ulmann

Weber

et al. 2021

Proc. ACM Program. Lang.

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Presburger arithmetic provides the mathematical core for the polyhedral compilation techniques that drive analytical cache models, loop optimization for ML and HPC, formal verification, and even hardware design. Polyhedral compilation is widely regarded as being slow due to the potentially high computational cost of the underlying Presburger libraries. Researchers typically use these libraries as powerful black-box tools, but the perceived internal complexity of these libraries, caused by the use of C as the implementation language and a focus on end-user-facing documentation, holds back broader performance-optimization efforts. With FPL, we introduce a new library for Presburger arithmetic built from the ground up in modern C++. We carefully document its internal algorithmic foundations, use lightweight C++ data structures to minimize memory management costs, and deploy transprecision computing across the entire library to effectively exploit machine integers and vector instructions. On a newly-developed comprehensive benchmark suite for Presburger arithmetic, we show a 5.4x speedup in total runtime over the state-of-the-art library isl in its default configuration and 3.6x over a variant of isl optimized with element-wise transprecision computing. We expect that the availability of a well-documented and fast Presburger library will accelerate the adoption of polyhedral compilation techniques in production compilers.

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Section: Related Workmentioning

confidence: 99%

FPL: fast Presburger arithmetic through transprecision

Pitchanathan

Ulmann

Weber

et al. 2021

Proc. ACM Program. Lang.

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“…The polyhedral framework is an algebraic representation of "sufficiently regular" program parts, covering arithmetic expressions on arrays surrounded by static control flow [23]. It has been a cornerstone of loop optimization in the past three decades [3,8,14,22,32,70] and is integrated into production compilers [13,30,43,62]. Despite its deceiving apparent simplicity, it covers a large class of computationally intensive kernels.…”

Section: Tensor Comprehensions Workflowmentioning

confidence: 99%

“…The polyhedral framework of compilation emerged as a natural candidate to design a versatile optimization flow satisfying the needs of the domain and target hardware. It has demonstrated strong results in domain-specific optimization [5,9,20,46], expert-driven meta-programming [6,15,26], embedding of third-party library code [40], and automatic generation of efficient code for heterogeneous targets [5,7,43,51,70,77]. We attempt to take the best of both worlds, defining a domain-specific language rich enough to capture full sub-graphs of modern Machine Learning (ML) models while enabling aggressive compilation competitive to native libraries.…”

Section: Introductionmentioning

confidence: 99%

“…The TC flow is also portable to other ML frameworks with a few lines of code. While our initial implementation focuses on Nvidia GPUs, the core technology applies to other types of accelerators with shared or partitioned memory [43,51,70,76]; these include vector and SIMD accelerators and also the generation of computational patterns suitable for ASICs with systolic designs and efficient storage management involving non-volatile memory technologies.…”

Section: Introductionmentioning

confidence: 99%

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The Next 700 Accelerated Layers

Vasilache

Zinenko

Theodoridis

et al. 2019

ACM Trans. Archit. Code Optim.

Self Cite

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Deep learning frameworks automate the deployment, distribution, synchronization, memory allocation, and hardware acceleration of models represented as graphs of computational operators. These operators wrap high-performance libraries such as cuDNN or NNPACK. When the computation does not match any predefined library call, custom operators must be implemented, often at high engineering cost and performance penalty, limiting the pace of innovation. To address this productivity gap, we propose and evaluate: (1) a domain-specific language with a tensor notation close to the mathematics of deep learning; (2) a Just-In-Time optimizing compiler based on the polyhedral framework; (3) carefully coordinated linear optimization and evolutionary algorithms to synthesize high-performance CUDA kernels; (4) the transparent integration of our flow into PyTorch and Caffe2, providing the fully automatic synthesis of high-performance GPU kernels from simple tensor algebra. The performance is comparable to, and often exceeds the performance of, highly tuned libraries. CCS Concepts: • Software and its engineering → Compilers;

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PolyGLoT: A Polyhedral Loop Transformation Framework for a Graphical Dataflow Language

Bhaskaracharya

Bondhugula

2013

Lecture Notes in Computer Science

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Abstract. Polyhedral techniques for program transformation are now used in several proprietary and open source compilers. However, most of the research on polyhedral compilation has focused on imperative languages such as C, where the computation is specified in terms of statements with zero or more nested loops and other control structures around them. Graphical dataflow languages, where there is no notion of statements or a schedule specifying their relative execution order, have so far not been studied using a powerful transformation or optimization approach. The execution semantics and referential transparency of dataflow languages impose a different set of challenges. In this paper, we attempt to bridge this gap by presenting techniques that can be used to extract polyhedral representation from dataflow programs and to synthesize them from their equivalent polyhedral representation. We then describe PolyGLoT, a framework for automatic transformation of dataflow programs which we built using our techniques and other popular research tools such as Clan and Pluto. For the purpose of experimental evaluation, we used our tools to compile LabVIEW, one of the most widely used dataflow programming languages. Results show that dataflow programs transformed using our framework are able to outperform those compiled otherwise by up to a factor of seventeen, with a mean speed-up of 2.30× while running on an 8-core Intel system.

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R-Stream Compiler

Cited by 9 publications

References 0 publications

FPL: fast Presburger arithmetic through transprecision

FPL: fast Presburger arithmetic through transprecision

The Next 700 Accelerated Layers

PolyGLoT: A Polyhedral Loop Transformation Framework for a Graphical Dataflow Language

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