RT Journal Article
JF Computer
YR 2009
VO 42
IS 2
SP 62
TI Refactoring for Data Locality
A1 K. Beyls,
A1 E.H. D'Hollander,
K1 Particle measurements
K1 Fuses
K1 Programming profession
K1 Optimizing compilers
K1 Program processors
K1 Size measurement
K1 Tracking loops
K1 Data visualization
K1 SPEC2000 benchmarks
K1 software engineering
K1 development tools
K1 programming languages
K1 processors
K1 operating systems
K1 visualization
K1 simulation
K1 computing methodologies
K1 compilers
K1 benchmarks
AB Refactoring for data locality opens a new avenue for performance-oriented program rewriting. SLO has broken down a large part of the complexity that software developers face when speeding up programs with numerous cache misses. Therefore, we consider SLO to belong to a new generation of program analyzers. Whereas existing cache profilers (generation 1.0) highlight problems such as cache misses, second-generation analyzers (such as SLO) highlight the place to fix problems. Improving data locality is also important in hardware-based applications. SLO was already used to optimize the frame rate and energy consumption in a wavelet decoder implemented on an FPGA. In another vein, the SLO concepts could be incorporated in interactive performance debuggers and profile-directed compilers. We believe that SLO will be useful in the optimization of many data-intensive applications.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 0018-9162
LA English
DO 10.1109/MC.2009.57
LK http://doi.ieeecomputersociety.org/10.1109/MC.2009.57