About Aart J. C. Bik

Aart J.C. Bik received his PhD degree from the Leiden University in 1996. He has worked as Principal Engineer at Intel Corp., where he received the 2002 Intel Achievement Award (highest company award) for making the Streaming SIMD Extensions easy to use through automatic vectorization. In 2007, Aart moved to Google Inc. Here, he has worked on Pregel, a distributed system for large-scale graph computations, in Google[x] on the GDK (Glass Development Kit), and on ART (Android RunTime). Aart received the 2017 Silver Perfy Award from Google for his work on SIMDization in the ART compiler.

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The Software Vectorization Handbook (LaTeX Edition) May 25, 2018

by Aart J.C. Bik

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The growing popularity of multimedia extensions renewed the interest in vectorizing compilers, which date back to the early days of supercomputers (with a traditional focus on Fortran programs). Although the target architectures of these early vectorizing compilers differ substantially from modern microprocessors with multimedia extensions, a lot of the vectorization methodology developed in the past can be put to use in today's compilers that target multimedia instruction sets that are based on the Single-Instruction-Multiple-Data (SIMD) paradigm. This book provides a detailed overview of compiler optimizations that convert sequential code into a form that best exploits multimedia extensions.

Note: this is a LaTeX-generated version of the original 2004 printed version of this book (now offered since Intel Press is no longer in operation) and covers Intel MMX Technology and Streaming SIMD Extensions (SSE, SSE2, SSE3).

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Compiler Support for Sparse Matrix Computations: PhD Thesis by Aart J.C. Bik, Leiden University, 1996 May 26, 2018

by Aart J.C. Bik

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This PhD Thesis by Aart J.C. Bik (Leiden University, 1996) gives an in-depth presentation of the sparse compiler MT1, i.e., a special kind of source-to-source restructuring compiler that can automatically transform a dense program (in which all operations on matrices are implemented using two-dimensional arrays) into a semantically equivalent sparse program (operating on more complicated sparse data structures), thereby reducing storage requirements and computational time of the original application.

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