Sparse matrix-vector multiplication (SpMV) is a critical building block for iterative graph analytics algorithms. Typically, such algorithms have a varying active vertex set across iterations. This variablity has been used to improve performance by either dynamically switching algorithms between iterations (software) or designing custom accelerators (hardware) for graph analytics algorithms. In this work, we propose a novel framework, CoSPARSE, that employs hardware and software reconfiguration as a synergistic solution to accelerate SpMV-based graph analytics algorithms. Building on previously proposed general-purpose reconfigurable hardware, we implement CoSPARSE as a software layer, abstracting the hardware as a specialized SpMV accelerator. CoSPARSE dynamically selects software and hardware configurations for each iteration and achieves a maximum speedup of 2.0× compared to the naïve implementation with no reconfiguration. Across a suite of graph algorithms, CoSPARSE outperforms a state-of-the-art shared memory framework, Ligra, on a Xeon CPU with up to 3.51× better performance and 877× better energy efficiency.

Modern programming languages provide programmers with rich abstractions for data collections as part of their standard libraries, e.g. Containers in the C++ STL, the Java Collections Framework, or the Scala Collections API. Typically, these collections frameworks are organised as hierarchies that provide programmers with common abstract data types (ADTs) like lists, queues, and stacks. While convenient, this approach introduces problems which ultimately affect application performance due to users over-specifying collection data types limiting implementation flexibility. In this paper, we develop Collection Skeletons which provide a novel, declarative approach to data collections. Using our framework, programmers explicitly select properties for their collections, thereby truly decoupling specification from implementation. By making collection properties explicit immediate benefits materialise in form of reduced risk of over-specification and increased implementation flexibility. We have prototyped our declarative abstractions for collections as a C++ library, and demonstrate that benchmark applications rewritten to use Collection Skeletons incur little or no overhead. In fact, for several benchmarks, we observe performance speedups (on average between 2.57 to 2.93, and up to 16.37) and also enhanced performance portability across three different hardware platforms.

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