Bringing Parallel Performance to Python with Domain-Specific Selective Embedded Just-in-Time Specialization

Today's productivity programmers, such as scientists who need to write code to do science, are typically forced to choose between productive and maintainable code with modest performance (e.g. Python plus native libraries such as SciPy SciPy) or complex, brittle, hardware-specific code that entangles application logic with performance concerns but runs two to three orders of magnitude faster (e.g. C++ with OpenMP, CUDA, etc.). The dynamic features of modern productivity languages like Python enable an alternative approach that bridges the gap between productivity and performance. SEJITS (Selective, Embedded, Just-in-Time Specialization) embeds domain-specific languages (DSLs) in high-level languages like Python for popular computational kernels such as stencils, matrix algebra, and others. At runtime, the DSLs are \textquotedbl{}compiled\textquotedbl{} by combining expert-provided source code templates specific to each problem type, plus a strategy for optimizing an abstract syntax tree representing a domain-specific but language-independent representation of the problem instance. The result is efficiency-level (e.g. C, C++) code callable from Python whose performance equals or exceeds that of handcrafted code, plus performance portability by allowing multiple code generation strategies within the same specializer to target different hardware present at runtime, e.g. multicore CPUs vs. GPUs. Application writers never leave the Python world, and we do not assume any modification or support for parallelism in Python itself.