Tools for High Performance Computing 2015: Proceedings of by Andreas Knüpfer, Tobias Hilbrich, Christoph Niethammer, José

By Andreas Knüpfer, Tobias Hilbrich, Christoph Niethammer, José Gracia, Wolfgang E. Nagel, Michael M. Resch

High functionality Computing (HPC) is still a driving force that gives large potentials and advantages for technological know-how and society. despite the fact that, a profound knowing of the computational concerns and really expert software program is required to reach at powerful and effective simulations. devoted software program instruments are very important elements of the HPC software program panorama, and help software builders. although a device is by way of definition no longer part of an software, yet particularly a supplemental piece of software program, it will probably make a basic distinction throughout the improvement of an program. Such instruments relief software builders within the context of debugging, functionality research, and code optimization, and hence make a big contribution to the advance of sturdy and effective parallel software program. This booklet introduces a variety of the instruments offered and mentioned on the ninth overseas Parallel instruments Workshop held in Dresden, Germany, September 2-3, 2015, which provided a longtime discussion board for discussing the newest advances in parallel tools.

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Extra resources for Tools for High Performance Computing 2015: Proceedings of the 9th International Workshop on Parallel Tools for High Performance Computing, September 2015, Dresden, Germany

Example text

2 and the optimization of Sort is highlighted in Sect. 3. The third stage of Trinity processing primarily involves Butterfly. An optimization of Butterfly would have implied a complete restructuring of the Trinity code, which was infeasible due to Trinity’s modular and constantly evolving pipeline. Nevertheless, the second stage recursively calls the main Trinity script, and therefore this stage benefits from our other optimization efforts as each individual de Bruijn graph is processed. In addition to the obvious optimization targets, we discovered an overhead of frequent forking and joining of parallel regions in ReadsToTranscipts marked by the sharp drops of parallel CPU utilization in the Collectl chart (Fig.

The first two kernels (load and store) perform pure loading and storing of data to/from the CPU core to the selected cache level or the memory. A combination of both is applied in the copy test. The last three tests are related to scientific computing and well understood. They range from the linear combination of two vectors called daxpy calculating A[i] = B[i] · c + A[i], a stream triad with formula A[i] = B[i] · c + C[i] to a vector triad computing A[i] = B[i] · C[i] + D[i]. The next pattern we look at is load imbalance.

Pattern driven node level performance engineering. pdf (2013), sC13 poster 19. : Performance patterns and hardware metrics on modern multicore processors: Best practices for performance engineering. Euro-Par 2012: Parallel Processing Workshops. Lecture Notes in Computer Science, vol. 7640, pp. 451–460. Springer, Berlin (2013) 20. : Non-determinism and overcount on modern hardware performance counter implementations. In: 2013 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp.

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