Paraprox: pattern-based approximation for data parallel applications

Approximate computing is an approach where reduced accuracy of results is traded off for increased speed, throughput, or both. Loss of accuracy is not permissible in all computing domains, but there are a growing number of data-intensive domains where the output of programs need not be perfectly correct to provide useful results or even noticeable differences to the end user. These soft domains include multimedia processing, machine learning, and data mining/analysis. An important challenge with approximate computing is transparency to insulate both software and hardware developers from the time, cost, and difficulty of using approximation. This paper proposes a software-only system, Paraprox, for realizing transparent approximation of data-parallel programs that operates on commodity hardware systems. Paraprox starts with a data-parallel kernel implemented using OpenCL or CUDA and creates a parameterized approximate kernel that is tuned at runtime to maximize performance subject to a target output quality (TOQ) that is supplied by the user. Approximate kernels are created by recognizing common computation idioms found in data-parallel programs (e.g., Map, Scatter/Gather, Reduction, Scan, Stencil, and Partition) and substituting approximate implementations in their place. Across a set of 13 soft data-parallel applications with at most 10% quality degradation, Paraprox yields an average performance gain of 2.7x on a NVIDIA GTX 560 GPU and 2.5x on an Intel Core i7 quad-core processor compared to accurate execution on each platform.