Elastic computing: a framework for transparent, portable, and adaptive multi-core heterogeneous computing

Over the past decade, system architectures have started on a clear trend towards increased parallelism and heterogeneity, often resulting in speedups of 10x to 100x. Despite numerous compiler and high-level synthesis studies, usage of such systems has largely been limited to device experts, due to significantly increased application design complexity. To reduce application design complexity, we introduce elastic computing - a framework that separates functionality from implementation details by enabling designers to use specialized functions, called elastic functions, which enable an optimization framework to explore thousands of possible implementations, even ones using different algorithms. Elastic functions allow designers to execute the same application code efficiently on potentially any architecture and for different runtime parameters such as input size, battery life, etc. In this paper, we present an initial elastic computing framework that transparently optimizes application code onto diverse systems, achieving significant speedups ranging from 1.3x to 46x on a hyper-threaded Xeon system with an FPGA accelerator, a 16-CPU Opteron system, and a quad-core Xeon system.

[1]  Hyesoon Kim,et al.  Qilin: Exploiting parallelism on heterogeneous multiprocessors with adaptive mapping , 2009, 2009 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

[2]  Steven G. Johnson,et al.  FFTW: an adaptive software architecture for the FFT , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[3]  Frank Vahid,et al.  Codesign-extended applications , 2002, Proceedings of the Tenth International Symposium on Hardware/Software Codesign. CODES 2002 (IEEE Cat. No.02TH8627).

[4]  Alan D. George,et al.  RAT: a methodology for predicting performance in application design migration to FPGAs , 2007, HPRCTA.

[5]  W. Najjar,et al.  A Code Refinement Methodology for Performance-Improved Synthesis from C , 2006, 2006 IEEE/ACM International Conference on Computer Aided Design.

[6]  Lloyd Lewins.  Experience and results porting HPEC Benchmarks to MONARCH , 2007 .

[7]  Nikil D. Dutt,et al.  SPARK: a high-level synthesis framework for applying parallelizing compiler transformations , 2003, 16th International Conference on VLSI Design, 2003. Proceedings..

[8]  Giovanni De Micheli,et al.  Synthesis of hardware models in C with pointers and complex data structures , 2001, IEEE Trans. Very Large Scale Integr. Syst..

[9]  H. Peter Hofstee,et al.  Power efficient processor architecture and the cell processor , 2005, 11th International Symposium on High-Performance Computer Architecture.

[10]  Bradford L. Chamberlain,et al.  Parallel Programmability and the Chapel Language , 2007, Int. J. High Perform. Comput. Appl..

[11]  Graham R. Nudd,et al.  Pace—A Toolset for the Performance Prediction of Parallel and Distributed Systems , 2000, Int. J. High Perform. Comput. Appl..

[12]  Jack J. Dongarra,et al.  Automatically Tuned Linear Algebra Software , 1998, Proceedings of the IEEE/ACM SC98 Conference.

[13]  Volume Assp,et al.  ACOUSTICS. SPEECH. AND SIGNAL PROCESSING , 1983 .

[14]  Katherine Yelick,et al.  OSKI: A library of automatically tuned sparse matrix kernels , 2005 .

[15]  H. Simmler,et al.  Strategic Challenges for Application Development Productivity in Reconfigurable Computing , 2008, 2008 IEEE National Aerospace and Electronics Conference.

[16]  Frank Vahid,et al.  A quantitative analysis of the speedup factors of FPGAs over processors , 2004, FPGA '04.

[17]  Milind Girkar,et al.  Extracting task-level parallelism , 1995, TOPL.

[18]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[19]  Katherine A. Yelick,et al.  A performance analysis of the Berkeley UPC compiler , 2003, ICS '03.

[20]  Franco Fummi,et al.  SystemC: a homogenous environment to test embedded systems , 2001, CODES '01.

[21]  David R. Musser,et al.  Introspective Sorting and Selection Algorithms , 1997, Softw. Pract. Exp..

[22]  Michael R. Macedonia,et al.  The GPU Enters Computing's Mainstream , 2003, Computer.

[23]  Alan Edelman,et al.  PetaBricks: a language and compiler for algorithmic choice , 2009, PLDI '09.

[24]  André DeHon,et al.  The Density Advantage of Configurable Computing , 2000, Computer.

[25]  M. McCool.  Data-Parallel Programming on the Cell BE and the GPU using the RapidMind Development Platform , 2006 .