HELIX-UP: Relaxing program semantics to unleash parallelization

Campanoni, Simone; Holloway, Glenn; Wei, Gu-Yeon; Brooks, David

doi:10.1109/cgo.2015.7054203

Cited by 33 publications

(20 citation statements)

References 22 publications

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“…Many studies have introduced novel software techniques for approximation that reduce execution time and/or energy. The transformations include task skipping [Meng et al 2009[Meng et al , 2010Rinard 2006], loop perforation [Misailovic et al , 2010Sidiroglou-Douskos et al 2011], approximate function substitution [Ansel et al 2011;Baek and Chilimbi 2010;Zhu et al 2012], dynamic knobs (dynamically changing function version), reduction sampling [Goiri et al 2015;Zhu et al 2012], tuning floating-point operations [Rubio-González et al 2013;Schkufza et al 2014], and approximate parallelization [Campanoni et al 2015;]. These techniques have been shown to work well across a variety of application domains resilient to small errors.…”

Section: Hardware Sensitivitymentioning

confidence: 99%

ApproxHPVM: a portable compiler IR for accuracy-aware optimizations

Sharif

Srivastava

Huzaifa

et al. 2019

Proc. ACM Program. Lang.

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We propose ApproxHPVM, a compiler IR and system designed to enable accuracy-aware performance and energy tuning on heterogeneous systems with multiple compute units and approximation methods. ApproxH-PVM automatically translates end-to-end application-level quality metrics into accuracy requirements for individual operations. ApproxHPVM uses a hardware-agnostic accuracy-tuning phase to do this translation that provides greater portability across heterogeneous hardware platforms and enables future capabilities like accuracy-aware dynamic scheduling and design space exploration. ApproxHPVM incorporates three main components: (a) a compiler IR with hardware-agnostic approximation metrics, (b) a hardware-agnostic accuracy-tuning phase to identify error-tolerant computations, and (c) an accuracy-aware hardware scheduler that maps error-tolerant computations to approximate hardware components. As ApproxHPVM does not incorporate any hardware-specific knowledge as part of the IR, it can serve as a portable virtual ISA that can be shipped to all kinds of hardware platforms. We evaluate our framework on nine benchmarks from the deep learning domain and five image processing benchmarks. Our results show that our framework can offload chunks of approximable computations to special-purpose accelerators that provide significant gains in performance and energy, while staying within user-specified application-level quality metrics with high probability. Across the 14 benchmarks, we observe from 1-9x performance speedups and 1.1-11.3x energy reduction for very small reductions in accuracy. CCS Concepts: • Software and its engineering → Compilers.

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Section: Hardware Sensitivitymentioning

confidence: 99%

ApproxHPVM: a portable compiler IR for accuracy-aware optimizations

Sharif

Srivastava

Huzaifa

et al. 2019

Proc. ACM Program. Lang.

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“…Other techniques reduce the number of computations to gain performance, e:g., loopperforation which removes some loop iterations based on profiling information [31]. Semantics relaxation can lead to better utilization of the capacity of the hardware, e:g., by extracting more parallelism [50]. Byna et al used approximation techniques on a sequential CPU algorithm to obtain a parallel algorithm with great performance on GPUs [51].…”

Section: Related Workmentioning

confidence: 99%

A language extension set to generate adaptive versions automatically

Schmitt¹,

Bastoul²,

Helluy³

2018

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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A large part of the development effort of compute-intensive applications is devoted to optimization, i.e., achieving the computation within a finite budget of time, space or energy. Given the complexity of modern architectures, writing simulation applications is often a two-step workflow. Firstly, developers design a sequential program for algorithmic tuning and debugging purposes. Secondly, experts optimize and exploit possible approximations of the original program to scale to the actual problem size. This second step is a tedious, time-consuming and error-prone task. In this paper we investigate language extensions and compiler tools to achieve that task semi-automatically in the context of approximate computing. We identified the semantic and syntactic information necessary for a compiler to automatically handle approximation and adaptive techniques for a particular class of programs. We propose a set of language extensions generic enough to provide the compiler with the useful semantic information when approximation is beneficial. We implemented the compiler infrastructure to exploit these extensions and to automatically generate the adaptively approximated version of a program. We provide an experimental study of the impact and expressiveness of our language extension set on various applications.

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“…SAGE [8] is a GPU-oriented technique skipping or simplifying processing with respect to performance impact while we primarily focus on accuracy. HELIX-UP [5] ignores some dependences to enable code parallelization, which is complementary to our approach. Power savings is also studied by Misailovic et al [27] who provide a language interface to rely on hardware providing approximate instruction and memory storage that draws less power but may produce a wrong result at a given rate.…”

Section: Comparison Against Loop Perforationmentioning

confidence: 99%

“…Relaxed semantics models are possible, e.g., to support commutativity to enable vectorization [4], however all input code iterations are to be executed in the optimized code. On the other hand, more aggressive techniques to automatically compute approximations have been designed, e.g., by ignoring some dependencies to enable parallelization [5], by providing alternative implementations of some code parts [6], or by skipping computations [7], [8]. In our work, we investigate a new way, called Adaptive Code Refinement (ACR), inspired by Adaptive Mesh Refinement [9], a classical numerical analysis technique providing the ability to dynamically tune a computational grid to achieve precise computation only where it matters.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Code Refinement: A Compiler Technique and Extensions to Generate Self-Tuning Applications

Schmitt

Helluy

Bastoul

2017

2017 IEEE 24th International Conference on High Performance Computing (HiPC)

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Abstract-Compiler high-level automatic optimization and parallelization techniques are well suited for some classes of simulation or signal processing applications, however they usually don't take into account domain-specific knowledge nor the possibility to change or to remove some computations to achieve "good enough" results. Differently, production simulation and signal processing codes have adaptive capabilities: they are designed to compute precise results only where it matters if the complete problem is not tractable or if computation time must be short. In this paper, we present a new way to provide adaptive capabilities to compute-intensive codes automatically. It relies on domain-specific knowledge provided through special pragmas by the programmer in the input code and on polyhedral compilation techniques to continuously regenerate at runtime a code that performs heavy computations only where it matters. We present experimental results on several applications where our strategy enables significant computation savings and speedup while maintaining a good precision, with a minimal effort from the programmer.

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HELIX-UP: Relaxing program semantics to unleash parallelization

Cited by 33 publications

References 22 publications

ApproxHPVM: a portable compiler IR for accuracy-aware optimizations

ApproxHPVM: a portable compiler IR for accuracy-aware optimizations

A language extension set to generate adaptive versions automatically

Adaptive Code Refinement: A Compiler Technique and Extensions to Generate Self-Tuning Applications

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