A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Design Patterns for Reconfigurable Computing
FCCM '04 Proceedings of the 12th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Evaluating MapReduce for Multi-core and Multiprocessor Systems
HPCA '07 Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
Mars: a MapReduce framework on graphics processors
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
FPGA Acceleration of RankBoost in Web Search Engines
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Map-reduce as a Programming Model for Custom Computing Machines
FCCM '08 Proceedings of the 2008 16th International Symposium on Field-Programmable Custom Computing Machines
CellMR: A framework for supporting mapreduce on asymmetric cell-based clusters
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Phoenix rebirth: Scalable MapReduce on a large-scale shared-memory system
IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)
MapCG: writing parallel program portable between CPU and GPU
Proceedings of the 19th international conference on Parallel architectures and compilation techniques
The RLOC is dead - long live the RLOC
Proceedings of the 19th ACM/SIGDA international symposium on Field programmable gate arrays
Benchmarking MapReduce Implementations for Application Usage Scenarios
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
G-Hadoop: MapReduce across distributed data centers for data-intensive computing
Future Generation Computer Systems
Tiled-MapReduce: Efficient and Flexible MapReduce Processing on Multicore with Tiling
ACM Transactions on Architecture and Code Optimization (TACO)
LINQits: big data on little clients
Proceedings of the 40th Annual International Symposium on Computer Architecture
HAT: history-based auto-tuning MapReduce in heterogeneous environments
The Journal of Supercomputing
Accelerate MapReduce on GPUs with multi-level reduction
Proceedings of the 5th Asia-Pacific Symposium on Internetware
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Machine learning and data mining are gaining increasing attentions of the computing society. FPGA provides a highly parallel, low power, and flexible hardware platform for this domain, while the difficulty of programming FPGA greatly limits its prevalence. MapReduce is a parallel programming framework that could easily utilize inherent parallelism in algorithms. In this paper, we describe FPMR, a MapReduce framework on FPGA, which provides programming abstraction, hardware architecture, and basic building blocks to developers. An on-chip processor scheduler is implemented to maximize the utilization of computation resources and achieve better load balancing. An efficient data access scheme is carefully designed to maximize data reuse and throughput. Meanwhile, the FPMR framework hides the task control, synchronization, and communication away from designers so that more attention can be paid to the application itself. A case study of RankBoost acceleration based on FPMR demonstrates that FPMR efficiently helps with the development productivity; and the speedup is 31.8x versus CPU-based implementation. This performance is comparable to a fully manually designed version, which achieves 33.5x speedup. Two other applications: SVM, PageRank are also discussed to show the generalization of the framework.