Tile size selection using cache organization and data layout
PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
Evaluating MapReduce for Multi-core and Multiprocessor Systems
HPCA '07 Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Scalable Parallel Programming with CUDA
Queue - GPU Computing
High performance discrete Fourier transforms on graphics processors
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Exploiting the Power of GPUs for Asymmetric Cryptography
CHES '08 Proceeding sof the 10th international workshop on Cryptographic Hardware and Embedded Systems
Mars: a MapReduce framework on graphics processors
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Phoenix rebirth: Scalable MapReduce on a large-scale shared-memory system
IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)
Accelerating SQL database operations on a GPU with CUDA
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
MapCG: writing parallel program portable between CPU and GPU
Proceedings of the 19th international conference on Parallel architectures and compilation techniques
Tiled-MapReduce: optimizing resource usages of data-parallel applications on multicore with tiling
Proceedings of the 19th international conference on Parallel architectures and compilation techniques
The Comparison of the Relative Entropy for Intrusion Detection on CPU and GPU
ICIS '10 Proceedings of the 2010 IEEE/ACIS 9th International Conference on Computer and Information Science
Phoenix++: modular MapReduce for shared-memory systems
Proceedings of the second international workshop on MapReduce and its applications
Using Shared Memory to Accelerate MapReduce on Graphics Processing Units
IPDPS '11 Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium
Multi-GPU MapReduce on GPU Clusters
IPDPS '11 Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium
Optimizing MapReduce for GPUs with effective shared memory usage
Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing
Accelerate MapReduce on GPUs with multi-level reduction
Proceedings of the 5th Asia-Pacific Symposium on Internetware
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In this paper, we present a new MapReduce framework, called Grex, designed to leverage general purpose graphics processing units (GPUs) for parallel data processing. Grex provides several new features. First, it supports a parallel split method to tokenize input data of variable sizes, such as words in e-books or URLs in web documents, in parallel using GPU threads. Second, Grex evenly distributes data to map/reduce tasks to avoid data partitioning skews. In addition, Grex provides a new memory management scheme to enhance the performance by exploiting the GPU memory hierarchy. Notably, all these capabilities are supported via careful system design without requiring any locks or atomic operations for thread synchronization. The experimental results show that our system is up to 12.4x and 4.1x faster than two state-of-the-art GPU-based MapReduce frameworks for the tested applications.