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Distinguished Talks

Application Experiments: MPPA and FPGA

Gilles Sassatelli

Speaker: Dr. Philip Top, Lawrence Livermore National Laboratory, USA
Dr. Maya Gokhale, Lawrence Livermore National Laboratory, USA


This paper describes the mapping approach, programmability, and performance of the Ambric Massively Parallel Processor Array (MPPA), and compares these aspects to an FPGA. Two application kernels, a trellis decoder, and n-gram frequency counter, were ported to the Ambric development system and an Altera Stratix II. We find that the mapping strategies to Ambric and FPGAs are similar at the high level, but diverge quite a bit in implementation due to differences in granularity between the basic compute units of the two devices. Both require substantial refactoring from the baseline sequential algorithm. The FPGA is a factor of 3-11x better in raw performance for the algorithms tested, but the Ambric fares significantly better than the FPGA in programmability and ease of application development.


Dr. Philip Top is an Electrical Engineer in the systems group at Lawrence Livermore National Laboratory. He was awarded a PhD in Electrical Engineering from Purdue University in 2007. Current research interests include embedded systems, high performance computing systems, and ultrawideband systems and signal processing. Philip is a member of the IEEE.

Dr. Pierre-André Mudry

Dr. Maya Gokhale is a Computer Scientist in the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory. Her research interests are in high performance embedded computing and reconfigurable computing, Maya is Associate Editor of the IEEE Transactions on Computers and the ACM Transactions on Reconfigurable Technology and Systems, co-author of the first book on Reconfigurable Computing, a member of Phi Beta Kappa and a Fellow of the IEEE.

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