Meanwhile, the computational heart of Arkouda is a relatively compact yet highly scalable Chapel interpreter that implements a powerful set of data science primitives. Functionally, this interpreter comprises a dispatcher, modular data transformations, and a zero-copy, in-memory object store, all implemented in about 12,000 lines of Chapel. While these components and design principles appear in other open-source projects, the competitive advantage of Arkouda comes from the unique position Chapel holds as a productive language with performance and scaling on par with industry-standard HPC technologies. For this reason, Arkouda is small enough to be maintainable while achieving good scaling on communication-intensive primitives (e.g. argsort) up to at least 512 nodes of a Cray XC.

The performance of the unstructured software is then studied and compared to a traditional open-source CFD software package programmed in C++ with MPI for communication (SU2). The results show that our Chapel implementation achieves performances similar to other CFD software written in C and C++, thus confirming that Chapel is a viable language for high-performance CFD applications.

In this talk we describe our early explorations of this space, based on miniTri, a miniapp from the Mantevo suite, and the mean hitting time algorithm, one of the analytics being explored within Grafiki, both of which are designed for use on distributed memory parallel processing environments. These implementations have been posed in terms of key linear algebra operations and algorithms, specifically sparse matrix-matrix multiplication, operating on integer datatypes, and the Conjugate Gradient method, based on a graph Laplacian matrix.