NOVA: A functional language for data parallelism

Functional languages provide a solid foundation on which complex optimization passes can be designed to exploit parallelism available in the underlying system. Their mathematical foundations enable high-level optimizations that would be impossible in traditional im-perative languages. This makes them uniquely suited for generation of efficient target code for parallel systems, such as multiple Central Processing Units (CPUs) or highly data-parallel Graphics Process-ing Units (GPUs). Such systems are becoming the mainstream for scientific and commodity desktop computing. Writing performance portable code for such systems using low-level languages requires significant effort from a human expert. This paper presents NOVA, a functional language and compiler for multi-core CPUs and GPUs. The NOVA language is a polymorphic, statically-typed functional language with a suite of higher-order functions which are used to express parallelism. These include map, reduce and scan. The NOVA compiler is a light-weight, yet powerful, optimizing compiler. It generates code for a variety of target platforms that achieve performance comparable to competing languages and tools, including hand-optimized code. The NOVA compiler is stand-alone and can be easily used as a target for higher-level or domain specific languages or embedded in other applications. We evaluate NOVA against two competing approaches: the Thrust library and hand-written CUDA C. NOVA achieves com-parable performance to these approaches across a range of bench-marks. NOVA-generated code also scales linearly with the number of processor cores across all compute-bound benchmarks