By Rosario Cammarota, Alexandru Nicolau, Alexander V. Veidenbaum (auth.), Hironori Kasahara, Keiji Kimura (eds.)
This publication constitutes the completely refereed post-conference lawsuits of the twenty fifth overseas Workshop on Languages and Compilers for Parallel Computing, LCPC 2012, held in Tokyo, Japan, in September 2012. The sixteen revised complete papers, five poster papers provided with 1 invited speak have been rigorously reviewed and chosen from 39 submissions. the point of interest of the papers is on following subject matters: compiling for parallelism, automated parallelization, optimization of parallel courses, formal research and verification of parallel courses, parallel runtime structures, task-parallel libraries, parallel software frameworks, functionality research instruments, debugging instruments for parallel courses, parallel algorithms and applications.
Read Online or Download Languages and Compilers for Parallel Computing: 25th International Workshop, LCPC 2012, Tokyo, Japan, September 11-13, 2012, Revised Selected Papers PDF
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Extra info for Languages and Compilers for Parallel Computing: 25th International Workshop, LCPC 2012, Tokyo, Japan, September 11-13, 2012, Revised Selected Papers
Sample text
Finally, ULM repeats the whole process thirty times to gather statistically rigorous Metarepetition Start Measure Measure / Repetitions = Actual Codelet Time Repetition Codelet ... movl sall addl movl -16(%ebp), %eax $2, %eax -20(%ebp), %eax %edx, (%eax) ... Stop Measure Fig. 2. Measurement process diagram 40 Y. C. Beyler, and W. Jalby execution data. ULM repeats the whole process thirty times to gather statistically rigorous execution data. One of the biggest problems in performance optimization is reproducibility.
Execution Time of UNAfold after simplifying reduction compared with the original implementation. The two lines shown are with slopes 4 and 3. the simplifying transformation was applied. The sequential code generator in AlphaZ code was used to generate the simplified version of fillMatrices 1 and replaced with the original function. 86GHz and 6GB of memory. Figure 2 shows the measured performance (raw and log-log). It clearly shows the reduction in complexity, and, as expected, the speedups with transformed code becomes greater and greater as the sequence length grows.
Generally efficient, -O3 can be a potential cause of sub-optimal performance, thus careful evaluation is vital to achieving maximum performance. The compiler research community proposed a practical approach. For example, Bodin et al. [6] describe the flag selection as an optimization problem in the transformation space. They introduced an iterative compilation technique and demonstrated a way to find an optimal combination after exploring less than 1% of the transformation space. Unfortunately, transformation space for modern compilers is large and keeps growing.