A Complete Description of the UnPython and Jit4GPU Compilation Framework
| dc.contributor.author | Amaral, Jose Nelson | |
| dc.contributor.author | Garg, Rahul | |
| dc.date.accessioned | 2025-05-01T21:13:01Z | |
| dc.date.available | 2025-05-01T21:13:01Z | |
| dc.date.issued | 2011 | |
| dc.description | Technical report TR11-05. A new compilation framework enables the execution of numerical-intensive applications in an execution environment that is formed by multi-core Central Processing Units (CPUs) and Graphics Processing Units (GPUs). A critical innovation is the use of a variation of Linear Memory Access Descriptors (LMADs) to analyze loop nests and determine automatically which memory locations must be transferred between the CPU address space and the GPU address space. In this programming model, the application is written in a combination of Python and NumPy, a rich numerical extension for Python. Inobstrusive light annotation is introduced to identify the type of function parameters and return values, and to indicate which loop nests should be parallelized and executed in the GPU. The new compilation system is a combination of an ahead-of-time compiler, unPython, to transform Python/NumPy code into a restricted C programming language notation, and a just-in-time compiler, jit4GPU, that converts this restricted C notation into the AMD CAL interface. The experimental evaluation using a collection of well-known benchmarks indicates that there is very significant performance advantages to execute important loops of numerical applications in GPUs. | TRID-ID TR11-05 | |
| dc.identifier.doi | https://doi.org/10.7939/R3CJ87M75 | |
| dc.language.iso | en | |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/ | |
| dc.subject | Software Systems | |
| dc.title | A Complete Description of the UnPython and Jit4GPU Compilation Framework | |
| dc.type | http://purl.org/coar/resource_type/c_93fc | |
| ual.jupiterAccess | http://terms.library.ualberta.ca/public |
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