ISA-independent workload characterization and its implications for specialized architectures

ISPASS 2013 - IEEE International Symposium on Performance Analysis of Systems and Software

Volumn , Issue , 2013, Pages 245-255

  Shao, Yakun Sophia ^a   Brooks, David ^a  

^a HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION PROGRAMS; ENERGY EFFICIENCY;

APPLICATION-CENTRIC; COMPUTING INDUSTRY; CONTROL FLOWS; DESIGN STYLES; ENERGY-EFFICIENT DESIGN; JIT COMPILER; WORKLOAD CHARACTERIZATION;

COMPUTER ARCHITECTURE;

EID: 84881437667     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISPASS.2013.6557175     Document Type: Conference Paper

References (19)

1. GCC4CLI. http://gcc.gnu.org/projects/cli.html.
2. GNU compiler collection (GCC) internals, GIMPLE documentation. http://gcc.gnu.orglonlinedocs/gcc-4.3.0/gccintlindex.html.
3. LLVM assembly language reference manual, bitcode documentation. http://llvm.org/docs/LangRef.html.
4. S. Bird, A. Phansalkar, L. K. John, A. Mercas, and R. Idukuru. Performance characterization of SPEC CPU benchmarks on Intel's Core microarchitecture based processor. In SPEC Benchmark Workshop, 2007 .
5. S. Campanoni, G. Agosta, S. Crespi-Reghizzi, and A. D. Biagio. A highly flexible, parallel virtual machine: Design and experience of lLDllT. Softw. Pract. Exper. , 20 1 0 .
6. L. Eeckhout, J. Sampson, and B . Calder. Exploiting program microarchitecture independent characteristics and phase behavior for reduced benchmark suite simulation. In International Symposium on Workload Characterization, 2005 .
7. L. Eeckhout, H. Vandierendonck, and K. D. Bosschere. Quantifying the impact of input data sets on program behavior and its applications . Journal of Instruction-Level Parallelism, 2003 .
8. K. Ganesan, L. John, V. Salapura, and J. Sexton. A performance counter based workload characterization on Blue Gene/P. In International Conference on Parallel Processing, 2008.
9. V. Govindaraju, e.-H. Ho, and K. Sankaralingam. Dynamically specialized datapaths for energy efficient computing. In International Symposium on High Petformance Computer Architecture, 201 1 .
10. R. Hameed, W. Qadeer, M . Wachs, O . Azizi, A . Solomatnikov, B . e . Lee, S. Richardson, e. Kozyrakis, and M. Horowitz. Understanding sources of inefficiency in general-purpose chips. In International Symposium on Computer Architecture, 20 1 0 .
11. K. Hoste and L. Eeckhout. Comparing benchmarks using key microarchitecture-independent characteristics . In International Symposium on Workload Characterization, 2006.
12. H.-H. S. Lee, M. Smelyanskiy, e. J. Newburn, and G. S. Tyson. Stack value file: custom microarchitecture for the stack. In International Symposium on High Petformance Computer Architecture, 200 1 .
13. e .-K. Luk, R . Cohn, R . Muth, H . Pati!, A . Klauser, G . Lowney, S . Wallace, V. J. Reddi, and K. Hazelwood. Pin: Building customized program analysis tools with dynamic instrumentation. In ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2005.
14. T. K. Prakash and L. Pengo Performance characterization of SPEC CPU2006 benchmarks on Intel Core 2 Duo processor. In International Conference on Parallel Processing, 2008.
15. e . Shannon. A mathematical theory of communication. Bell System Technical Journal, 1 948.
16. T. Sherwood, E. Perelman, G. Hamerly, and B . Calder. Automatically characterizing large scale program behavior. In International Conference on Architectural Support for Programming Languages and Operating Systems, 2002.
17. G. Venkatesh, J. Sampson, N. Goulding, S. Garcia, V. Bryksin, J. LugoMartinez, S. Swanson, and M. B. Taylor. Conservation cores: reducing the energy of mature computations . In International Conference on Architectural Support for Programming Languages and Operating Systems, 2010.
18. L. Yen, S. e. Draper, and M. D. Hill. Notary: Hardware techniques to enhance signatures. In International Symposium on Microarchitecture, 2008.
19. T. Yokota, K. Ootsu, and T. Baba. Introducing entropies for representing program behavior and branch predictor performance. In Workshop on Experimental Computer Science, 2007 .