Best-effort parallel execution framework for Recognition and mining applications

Recognition and mining (RM) applications are an emerging class of computing workloads that will be commonly executed on future multi-core and many-core computing platforms. The explosive growth of input data and the use of more sophisticated algorithms in RM applications will ensure, for the foreseeable future, a significant gap between the computational needs of RM applications and the capabilities of rapidly evolving multi- or many-core platforms. To address this gap, we propose a new parallel programming model that inherently embodies the notion of best-effort computing, wherein the underlying parallel computing environment is not expected to be perfect. The proposed best-effort programming model leverages three key characteristics of RM applications: (1) the input data is noisy and it often contains significant redundancy, (2) computations performed on the input data are statistical in nature, and (3) some degree of imprecision in the output is acceptable. As a specific instance of the best-effort parallel programming model, we describe an “iterative-convergence” parallel template, which is used by a significant class of RM applications. We show how best-effort computing can be used to not only reduce computational workload, but to also eliminate dependencies between computations and further increase parallelism. Our experiments on an 8-core machine demonstrate a speed-up of 3.5X and 4.3X for the K-means and GLVQ algorithms, respectively, over a conventional parallel implementation. We also show that there is almost no material impact on the accuracy of results obtained from best-effort implementations in the application context of image segmentation using K-means and eye detection in images using GLVQ.