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Kokkos is a programming model for writing performance portable applications for all major high performance computing platforms. It provides abstractions for data management and common parallel operations, allowing developers to write portable high performance code with minimal knowledge of architecture-specific details. Kokkos is implemented as a heavily-templated C++ library. However, C++ is not ideal for rapid prototyping and quick algorithmic exploration. An increasing number of developers use Python for scientific computing, machine learning, and data analytics. In this paper, we present a new Python framework, dubbed PyKokkos, for writing performance portable applications entirely in Python. PyKokkos provides Kokkos-like abstractions that are easier to use and more concise than the C++ interface. We implemented PyKokkos by building a translator from a subset of Python to C++ Kokkos and bridging necessary function calls via automatically generated Python bindings. PyKokkos is also compatible with NumPy, a widely-used high performance Python library. By porting several existing Kokkos applications to PyKokkos, including ExaMiniMD (∼3k lines of code in C++), we show that the latter can achieve efficient execution with low performance overhead. CCS CONCEPTS• Software and its engineering → Source code generation; • Computing methodologies → Parallel programming languages.

Bounded-exhaustive testing (BET), which exercises a program under test for all inputs up to some bounds, is an effective method for detecting software bugs. Systematic property-based testing is a BET approach where developers write test generation programs that describe properties of test inputs. Hybrid test generation programs offer the most expressive way to write desired properties by freely combining declarative filters and imperative generators. However, exploring hybrid test generation programs, to obtain test inputs, is both computationally demanding and challenging to parallelize. We present the first programming and execution models, dubbed Tempo, for parallel exploration of hybrid test generation programs. We describe two different strategies for mapping the computation to parallel hardware and implement them both for GPUs and CPUs. We evaluated Tempo by generating instances of various data structures commonly used for benchmarking in the BET domain. Additionally, we generated CUDA programs to stress test CUDA compilers, finding four bugs confirmed by the developers.

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