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On the Complexity of Robust Source-to-Source Translation from CUDA to OpenCL

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dc.contributor.authorSathre, Paul Danielen
dc.contributor.committeechairFeng, Wu-chunen
dc.contributor.committeememberGardner, Mark K.en
dc.contributor.committeememberTilevich, Elien
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2015-05-27T08:04:17Zen
dc.date.available2015-05-27T08:04:17Zen
dc.date.issued2013-06-12en
dc.description.abstractThe use of hardware accelerators in high-performance computing has grown increasingly prevalent, particularly due to the growth of graphics processing units (GPUs) as general-purpose (GPGPU) accelerators. Much of this growth has been driven by NVIDIA's CUDA ecosystem for developing GPGPU applications on NVIDIA hardware. However, with the increasing diversity of GPUs (including those from AMD, ARM, and Qualcomm), OpenCL has emerged as an open and vendor-agnostic environment for programming GPUs as well as other parallel computing devices such as the CPU (central processing unit), APU (accelerated processing unit), FPGA (field programmable gate array), and DSP (digital signal processor). The above, coupled with the broader array of devices supporting OpenCL and the significant conceptual and syntactic overlap between CUDA and OpenCL, motivated the creation of a CUDA-to-OpenCL source-to-source translator. However, there exist sufficient differences that make the translation non-trivial, providing practical limitations to both manual and automatic translation efforts. In this thesis, the performance, coverage, and reliability of a prototype CUDA-to-OpenCL source translator are addressed via extensive profiling of a large body of sample CUDA applications. An analysis of the sample body of applications is provided, which identifies and characterizes general CUDA source constructs and programming practices that obstruct our translation efforts. This characterization then led to more robust support for the translator, followed by an evaluation that demonstrated the performance of our automatically-translated OpenCL is on par with the original CUDA for a subset of sample applications when executed on the same NVIDIA device.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:808en
dc.identifier.urihttp://hdl.handle.net/10919/52631en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSource Translationen
dc.subjectClangen
dc.subjectCUDAen
dc.subjectOpenCLen
dc.subjectGPUen
dc.subjectGPGPUen
dc.subjectCompilersen
dc.titleOn the Complexity of Robust Source-to-Source Translation from CUDA to OpenCLen
dc.typeThesisen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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