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GTC ON-DEMAND

Big Data Analytics
Presentation
Media
Extending Python for High-Performance Data-Parallel Programming
Abstract:
Our objective is to design a high-level data-parallel language extension to Python on GPUs. This language extension cooperates with the CPython implementation and uses Python syntax for describing data-parallel computations. The combination of rich library support and language simplicity makes Python ideal for subject matter experts to rapidly develop powerful applications. Python enables fast turnaround time and flexibility for custom analytic pipelines to react to immediate demands. However, CPython has been criticized as being slow and the existence of the global interpreter lock (GIL) makes it difficult to take advantage of parallel hardware. To solve this problem, Continuum Analytics has developed LLVM based JIT compilers for CPython. Numba is the open-source JIT compiler. NumbaPro is the proprietary compiler that adds CUDA GPU support. We aim to extend and improve the current GPU support in NumbaPro to further increase the scalability and portability of Python-based GPU programming.
 
Topics:
Big Data Analytics, Programming Languages, Large Scale Data Analytics, Defense
Type:
Talk
Event:
GTC Silicon Valley
Year:
2014
Session ID:
S4608
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