UC Berkeley

This work examines a data-intensive irregular application from genomics, a long-read to long-read alignment problem, which represents a kind of Generalized N-Body problem, one of the "seven giants"of the NRC Big Data motifs [5]. In this problem, computations (genomic alignments) are performed on sparse and data-dependent pairs of inputs, with variable cost computation and variable datum sizes. In particular, there is no inherent locality in the pairwise interactions, unlike simulation-based N-Body problems, and the interaction sparsity depends on particular parameters of the input, which can also affect the quality of the output. We examine two extremes to distributed memory parallelization for this problem, bulk-synchrony and asynchrony, with real workloads. Our bulk-synchronous implementation, uses collective communication in MPI, while our asynchronous implementation uses cross-node RPCs in UPC++. We show that the asynchronous version effectively hides communication costs, with a memory footprint that is typically much lower than the bulk-synchronous version. Our application, while simple enough to be a kind of proxy for genomics or data analytics applications more broadly, is also part of a real application pipeline. It shows good scaling on real input problems, and at the same time, reveals some of the programming and architectural challenges for scaling this type of data-intensive irregular application.