Title :
Accelerating Big Data Analytics Using FPGAs
Author :
Neshatpour, Katayoun ; Malik, Maria ; Ghodrat, Mohammad Ali ; Homayoun, Houman
Author_Institution :
Dept. of Electr. & Comput. Eng., George Mason Univ., Fairfax, VA, USA
Abstract :
Emerging big data analytics applications require a significant amount of server computational power. As chips are hitting power limits, computing systems are moving away from general-purpose designs and toward greater specialization. Hardware acceleration through specialization has received renewed interest in recent years, mainly due to the dark silicon challenge. To address the computing requirements of big data, and based on the benchmarking and characterization results, we envision a data-driven heterogeneous architecture for next generation big data server platforms that leverage the power of field-programmable gate array (FPGA) to build custom accelerators in a Hadoop MapReduce framework. Unlike a full and dedicated implementation of Hadoop MapReduce algorithm on FPGA, we propose the hardware/software (HW/SW) co-design of the algorithm, which trades some speedup at a benefit of less hardware. Considering communication overhead with FPGA and other overheads involved in Hadoop MapReduce environment such as compression and decompression, shuffling and sorting, our experimental results show significant potential for accelerating Hadoop MapReduce machine learning kernels using HW/SW co-design methodology.
Keywords :
Big Data; benchmark testing; data analysis; field programmable gate arrays; hardware-software codesign; learning (artificial intelligence); parallel processing; FPGA; HW-SW codesign methodology; Hadoop MapReduce framework; Hadoop MapReduce machine learning kernels; big data analytics; big data computing requirements; computing systems; data-driven heterogeneous architecture; field-programmable gate array; general-purpose designs; hardware acceleration; hardware-software codesign; next generation big data server platforms; server computational power; Acceleration; Big data; Field programmable gate arrays; Hardware; Kernel; Machine learning algorithms; Servers; Big-data; FPGA; MapReduce; acceleration;
Conference_Titel :
Field-Programmable Custom Computing Machines (FCCM), 2015 IEEE 23rd Annual International Symposium on
Conference_Location :
Vancouver, BC
DOI :
10.1109/FCCM.2015.59