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University of Cambridge > Talks.cam > Computer Laboratory Computer Architecture Group Meeting > Map-reduce as a Programming Model for Custom Computing Machines
Map-reduce as a Programming Model for Custom Computing MachinesAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Prof Simon Moore. Note unusual time The map-reduce model requires users to express their problem in terms of a map function that processes single records in a stream, and a reduce function that merges all mapped outputs to produce a final result. By exposing structural similarity in this way, a number of key issues associated with the design of custom computing machines including parallelisation; design complexity; software-hardware partitioning; hardware-dependency, portability and scalability can be easily addressed. We present an implementation of a map-reduce library supporting parallel field programmable gate arrays (FPGAs)and graphics processing units (GPUs). Parallelisation due to pipelining, multiple datapaths and concurrent execution of FPGA /GPU hardware is automatically achieved. Users first specify the map and reduce steps for the problem in ANSI C and no knowledge of the underlying hardware or parallelisation is needed. The source code is then manually translated into a pipelined datapath which, along with the map-reduce library, is compiled into appropriate binary configurations for the processing units. We describe our experience in developing a number of benchmark problems in signal processing, Monte Carlo simulation and scientific computing as well as report on the performance of FPGA , GPU and heterogeneous systems. This talk is part of the Computer Laboratory Computer Architecture Group Meeting series. This talk is included in these lists:
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