Velociraptor

Garg, Rahul; Hendren, Laurie

doi:10.1145/2628071.2628097

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…Finally, Garg et al 31 developed a compiler toolkit for targeting GPGPUs called Velociraptor and integrated it with a JIT compiler for MATLAB called McVM 32 . The focus of their work was to develop an IR (called VRIR), agnostic to the target language, for GPU JIT compilation.…”

Section: Related Workmentioning

confidence: 99%

Compilation of MATLAB computations to CPU/GPU via C/OpenCL generation

Reis

Bispo

Cardoso

2020

Concurrency and Computation

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In order to take advantage of the processing power of current computing platforms, programmers typically need to develop software versions for different target devices. This task is time-consuming and requires significant programming and computer architecture expertise. A possible and more convenient alternative is to start with a single high-level description of a program with minimum implementation details, and generate custom implementations according to the target platform. In this paper, we use MATLAB as a high-level programming language and propose a compiler that targets CPU/GPU computing platforms by generating customized implementations in C and OpenCL. We propose a number of compiler techniques to automatically generate efficient C and OpenCL code from MATLAB programs. One of such compiler techniques relies on heuristics to decide when and how to use Shared Virtual Memory (SVM). The experimental results show that our approach is able to generate code that provides significant speedups (eg, geometric mean speedup of 11× for a set of simple benchmarks) using a discrete GPU over equivalent sequential C code executing on a CPU. With more complex benchmarks, for which only some code regions can be parallelized, and are thus offloaded, the generated code achieved speedups of up to 2.2×. We also show the impact of using SVM, specifically fine-grained buffers, and the results show that the compiler is able to achieve significant speedups, both over the versions without SVM and with naïve aggressive SVM use, across three CPU/GPU platforms.

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Section: Related Workmentioning

confidence: 99%

Compilation of MATLAB computations to CPU/GPU via C/OpenCL generation

Reis

Bispo

Cardoso

2020

Concurrency and Computation

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Boosting Java Performance Using GPGPUs

Clarkson

Kotselidis

Brown

et al. 2017

Architecture of Computing Systems - ARCS 2017

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Heterogeneous programming has started becoming the norm in order to achieve better performance by running portions of code on the most appropriate hardware resource. Currently, significant engineering efforts are undertaken in order to enable existing programming languages to perform heterogeneous execution mainly on GPUs. In this paper we describe Jacc, an experimental framework which allows developers to program GPGPUs directly from Java. By using the Jacc framework, developers have the ability to add GPGPU support into their applications with minimal code refactoring.To simplify the development of GPGPU applications we allow developers to model heterogeneous code using two key abstractions: tasks, which encapsulate all the information needed to execute code on a GPGPU; and task graphs, which capture the intertask control-flow of the application. Using this information the Jacc runtime is able to automatically handle data movement and synchronization between the host and the GPGPU; eliminating the need for explicitly managing disparate memory spaces.In order to generate highly parallel GPGPU code, Jacc provides developers with the ability to decorate key aspects of their code using annotations. The compiler, in turn, exploits this information in order to automatically generate code without requiring additional code refactoring.Finally, we demonstrate the advantages of Jacc, both in terms of programmability and performance, by evaluating it against existing Java frameworks. Experimental results show an average performance speedup of 32x and a 4.4x code decrease across eight evaluated benchmarks on a NVIDIA Tesla K20m GPU.

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