A TOOLKIT FOR BUILDING DYNAMIC COMPILERS FOR ARRAY-BASED LANGUAGES TARGETING CPUS AND GPUS

Array-based languages such as MATLAB and Python (with NumPy) have become very popular for scientific computing. However, the performance of the implementations of these languages is often lacking. For example, some of the implementations are interpreted. Further, these languages were not designed with multi-core CPUs and GPUs in mind and thus don’t take full advantage of modern hardware. Thus, developing just-in-time (JIT) compilers for these languages that allow scientific programmers to efficiently target both CPUs and GPUs is of increasing interest. However building such compilers requires considerable effort. Prior to this thesis, there were no reusable compiler toolkits for array-based languages even though many of the compilation challenges are similar across languages. This thesis is about a set of two novel and reusable tools, Velociraptor and RaijinCL, that simplify the work of building JIT compilers for array-based languages targeting both CPUs and GPUs. Velociraptor is a reusable, embeddable dynamic compiler toolkit while RaijinCL is an auto-tuning high-performance matrix operations library