A configurable mapreduce accelerator for multi-core FPGAs (abstract only)

Abstract

MapReduce is a widely used programming framework for the implementation of cloud computing application in data centers. This work presents a novel configurable hardware accelerator that is used to speed up the processing of multi-core and cloud computing applications based on the MapReduce programming framework. The proposed MapReduce configurable accelerator is augmented to multi-core processors and it performs a fast indexing and accumulation of the key/value pairs based on an efficient memory architecture using Cuckoo hashing. The MapReduce accelerator consists of the memory buffers that store the key/value pairs, and the processing units that are used to accumulate the key's value sent from the processors. In essence, this accelerator is used to alleviate the processors from executing the Reduce tasks, and thus executing only the Map tasks and emitting the intermediate key/value pairs to the hardware acceleration unit that performs the Reduce operation. The number and the size of the keys that can be stored on the accelerator are configurable and can be configured based on the application requirements. The MapReduce accelerator has been implemented and mapped to a multi-core FPGA with embedded ARM processors (Xilinx Zynq FPGA) and has been integrated with the MapReduce programming framework under Linux. The performance evaluation shows that the proposed accelerator can achieve up to 1.8x system speedup of the MapReduce applications and hence reduce significantly the execution time of multi-core and cloud computing applications. (Action: "Supporting Postdoctoral Researchers", "Education and Lifelong Learning" Program (GSRT) and co-financed by the ESF and the Greek State.)