Performance-energy efficiency model of heterogeneous parallel multicore system

Energy-efficiency is one of the most challenges of designing future heterogeneous multicore system, beyond performance, hereby we propose a performance-energy efficiency analytical model for integrated heterogeneous parallel multicore system which is promising to be used for big data applications. The model extends the traditional computing-centric model by considering the overhead of data preparation which can not be neglected in heterogenous multicore processor system any more. The analysis clearly shows that higher parallelism gained from either computation or data preparation brings greater energy-efficiency. Improving the performance-energy efficiency of data preparation is another promising approach to affect power consumption. Therefore, more informed tradeoffs should be taken when we design a modern heterogeneous processor system within limited budget of energy(power).

[1]  Scott A. Mahlke,et al.  Heterogeneous microarchitectures trump voltage scaling for low-power cores , 2014, 2014 23rd International Conference on Parallel Architecture and Compilation (PACT).

[2]  Ami Marowka.  Extending Amdahl's Law for Heterogeneous Computing , 2012, 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications.

[3]  Brad Cohen,et al.  Kabini: An AMD Accelerated Processing Unit System on A Chip , 2014, IEEE Micro.

[4]  Hao Che,et al.  Amdahl's law for multithreaded multicore processors , 2014, J. Parallel Distributed Comput..

[5]  Hsien-Hsin S. Lee,et al.  Extending Amdahl's Law for Energy-Efficient Computing in the Many-Core Era , 2008, Computer.

[6]  L. Sawalha,et al.  Energy-Efficient Phase-Aware Scheduling for Heterogeneous Multicore Processors , 2012, 2012 IEEE Green Technologies Conference.

[7]  Mark D. Hill,et al.  Amdahl's Law in the Multicore Era , 2008, Computer.

[8]  Guibin Wang,et al.  Power-Efficient Work Distribution Method for CPU-GPU Heterogeneous System , 2010, International Symposium on Parallel and Distributed Processing with Applications.

[9]  Rong Ge,et al.  Power-Aware Speedup , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[10]  Pei Songwe.  An approach to accessing unified memory address space of heterogeneous kilo-cores system , 2015 .

[11]  G. Amdhal,et al.  Validity of the single processor approach to achieving large scale computing capabilities , 1967, AFIPS '67 (Spring).

[12]  M. P. Bekakos,et al.  Performance scalability and energy consumption on distributed and many-core platforms , 2014, The Journal of Supercomputing.

[13]  Alejandro Duran,et al.  The Intel® Many Integrated Core Architecture , 2012, 2012 International Conference on High Performance Computing & Simulation (HPCS).

[14]  Sebastian M. Londono,et al.  Extending Amdahl's law for energy-efficiency , 2010, 2010 International Conference on Energy Aware Computing.

[15]  Fabian Nowak.  Evaluating the Energy Efficiency of Reconfigurable Computing Toward Heterogeneous Multi-Core Computing , 2014, ARCS Workshops.

[16]  Xian-He Sun,et al.  Reevaluating Amdahl's law in the multicore era , 2010, J. Parallel Distributed Comput..

[17]  Dohoon Kim,et al.  A Performance-Energy Model to Evaluate Single Thread Execution Acceleration , 2015, IEEE Computer Architecture Letters.

[18]  Phil Rogers,et al.  Heterogeneous system architecture overview , 2013, 2013 IEEE Hot Chips 25 Symposium (HCS).

[19]  Naixue Xiong,et al.  Fusion Coherence: Scalable Cache Coherence for Heterogeneous Kilo-Core System , 2014, ACA.