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Hashing is one of the most fundamental operations that provides a means for a program to obtain fast access to large amounts of data. Despite the emergence of GPUs as many-threaded general purpose processors, high performance parallel data hashing solutions for GPUs are yet to receive adequate attention. Existing hashing solutions for GPUs not only impose restrictions (e.g., inability to concurrently...
The vast computing power of GPUs makes them an attractive platform for accelerating large scale data parallel computations such as popular graph processing applications. However, the inherent irregularity and large sizes of real-world power law graphs makes effective use of GPUs a major challenge. In this paper we develop techniques that greatly enhance the performance and scalability of vertex-centric...
GPU's SIMD architecture is a double-edged sword confronting parallel tasks with control flow divergence. On the one hand, it provides a high performance yet power-efficient platform to accelerate applications via massive parallelism; however, on the other hand, irregularities induce inefficiencies due to the warp's lockstep traversal of all diverging execution paths. In this work, we present a software...
The disparity between the CPU and GPU domains has forced the programmers to adhere to the traditional driver-based GPU programming approach. The negative implications of this approach are inter-domain data transfer overhead, host memory pressure and CPU underutilization. In this paper, we propose a novel hybrid shared memory-based execution approach to enhance the throughput of the General Purpose...
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