veyepar: Bringing High Performance to Python/Numpy Without Changing a Single Line of Code.

Author(s):	Simon Andreas Frimann Lund, Mads Ruben Burgdorff Kristensen, Brian Vinter, Troels Blum
Location	106
Date	jul Wed 18	Days Raw Files
Start	14:45	First Raw Start	14:23
Duration	20:00	Offset	0:21:43
End	15:05	Last Raw End	15:17
Chapters	00:00 0:20:09
Total cuts_time	26 min.

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Recent years have provided a wealth of projects showing that using Python for scientific applications outperforms even popular choices such as Matlab. A major factor driving these successes is the efficient utilization of multi-cores, GPUs for general-purpose computation and scaling computations to clusters.

However, often these advances sacrifice some of the high-productivity features of Python by introducing new language constructs, enforcing new language semantics and/or enforcing explicit data types. The result is that the user will have to rewrite existing Python applications to use the Python extension.

In order to use GPGPUs in Python, a popular approach is to embed CUDA/OpenCL code kernels directly in the Python application. The programming productivity of this approach is better and more readable than C/C++ applications but it is still inferior to native Python code. Furthermore, the approach enforces hardware specific programming and thus requires intimate knowledge of the underlying hardware and the CUDA/OpenCL programming model.

Copenhagen Vector Byte Code (cphVB) strives to provide a high-performance back-end for Numerical Python (NumPy) without reducing the high-productivity of Python/NumPy. Without any involvement of the user, cphVB will transform regular sequential Python/NumPy applications into high-performance applications. The cphVB runtime system is capable of utilizing a broad range of computing platforms efficiently, e.g. Multi-core CPUs, GPGPUs and clusters of such machines.

cphVB consists of a bridge that translates NumPy array operations into cphVB vector operations. The bridge will send these vector operations to a Vector Engine that performs the actual execution of the operations. cphVB comes with a broad range of Vector Engines optimized to specific hardware architectures, such as multi-core CPUs, GPGPU and clusters of said architectures. Thus, cphVB provides a high-productivity, high-performance framework that support legacy NumPy applications without changing a single line of code.

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Simon Andreas Frimann Lund, Mads Ruben Burgdorff Kristensen, Brian Vinter, Troels Blum

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Simon Andreas Frimann Lund, Mads Ruben Burgdorff Kristensen, Brian Vinter, Troels Blum

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