Abstract
AbstractIn this paper, we introduce JIT compilation for the high-productivity framework Python/NumPy in order to boost the performance significantly. The JIT compilation of Python/NumPy is completely transparent to the user – the runtime system will automatically JIT compile and execute the NumPy instructions encountered in a Python application. In other words, we introduce a framework that provides the high-productivity from Python while maintaining the high-performance of a low-level, compiled language.We transforms NumPy vector instruction into an Abstract Syntax Tree representation that creates the basis for further optimizations. From the AST we auto-generate C code which we compile into computational kernels and execute. These incorporate temporary array removal and loop-fusion which are main benefactors in the achieved speedups. In order to amortize the overhead of creation, we also implement a cache for the compiled kernels.We evaluate the JIT compilation by executing several scientific computing benchmarks on an AMD. Compared to NumPy, we achieve speedups of a factor 4.72 for a N-Body application and 7.51 for a Jacobi Stencil application executing on a single CPU core.
Highlights
Many scientific algorithms can be expressed by using vector operation and linear algebra
In order to improve the performance of Python/NumPy, we introduce a Just-In-Time (JIT) compiler backend for the NumPy library
We have implemented a JIT framework for Python/NumPy that allow NumPy instructions to be expressed in an abstract form using Abstract Syntax Tree’s. This has allowed for a set of optimizations to the computations of Numpy vector operations and enables further optimizations
Summary
Many scientific algorithms can be expressed by using vector operation and linear algebra. These are expressed in specialized high-level languages such as the NumPy library for Python. Their performance is often significantly lower than when implemented and computed in a low-level language. Expressing the data and calculations efficiently in a lowlevel language such as C is far from being a trivial task. It requires an in-depth understanding to implement this efficiently on heterogeneous hardware architectures
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