Best-effort semantic document search on GPUs
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
Two experiments with application-level quality of service on the EGEE grid
Proceedings of the 2nd workshop on Grids meets autonomic computing
Scalable effort hardware design: exploiting algorithmic resilience for energy efficiency
Proceedings of the 47th Design Automation Conference
Best-effort computing: re-thinking parallel software and hardware
Proceedings of the 47th Design Automation Conference
Discovering Piecewise Linear Models of Grid Workload
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Language virtualization for heterogeneous parallel computing
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Managing performance vs. accuracy trade-offs with loop perforation
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Towards Non-Stationary Grid Models
Journal of Grid Computing
Randomized accuracy-aware program transformations for efficient approximate computations
POPL '12 Proceedings of the 39th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Proceedings of the 2012 ACM workshop on Relaxing synchronization for multicore and manycore scalability
Parallelizing Sequential Programs with Statistical Accuracy Tests
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on Probabilistic Embedded Computing
Managing the Quality vs. Efficiency Trade-off Using Dynamic Effort Scaling
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on Probabilistic Embedded Computing
Analysis and characterization of inherent application resilience for approximate computing
Proceedings of the 50th Annual Design Automation Conference
Quality programmable vector processors for approximate computing
Proceedings of the 46th Annual IEEE/ACM International Symposium on Microarchitecture
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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.