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Applications in High-Performance Computing (HPC) environments face challengesdue to increasing complexity. Among them, the increasing usage of sparse datapushes the limits of data structures and programming models and hampers theefficient usage of existing, highly parallel hardware. The GraphBLASspecification tackles these challenges by proposing a set of data containersand primitives, coupled with a semantics based on abstract algebraic concepts:this allows multiple applications on sparse data to be described with a smallset of primitives and benefit from the many optimizations of acompile-time-known algebraic specification. Among HPC applications, the HighPerformance Conjugate Gradient (HPCG) benchmark is an important representativeof a large body of sparse workloads, and its structure poses severalprogrammability and performance challenges. This work tackles them by proposingand evaluating an implementation on GraphBLAS of HPCG, highlighting the mainchanges to its kernels. The results for shared memory systems outperforms thereference, while results in distributed systems highlight fundamentallimitations of GraphBLAS-compliant implementations, which suggests severalfuture directions.

Effective implementation of the High Performance Conjugate Gradient benchmark on GraphBLAS