3 research outputs found
The limits of speculative trace reuse on deeply pipelined processors
Trace reuse improves the performance of processors by skipping the execution of sequences of redundant instructions. However, many reusable traces do not have all of their inputs ready by the time the reuse test is done. For these cases, we developed a new technique called reuse through speculation on traces (RST), where trace inputs may be predicted. We study the limits of RST for modern processors with deep pipelines, as well as the effects of constraining resources on performance. We show that our approach reuses more traces than the nonspeculative trace reuse technique, with speedups of 43% over a nonspeculative trace reuse and 57% when memory accesses are reused
On The Energy Efficiency And Performance Of Irregular Application Executions On Multicore, Numa And Manycore Platforms
Until the last decade, performance of HPC architectures has been almost exclusively quantified by their processing power. However, energy efficiency is being recently considered as important as raw performance and has become a critical aspect to the development of scalable systems. These strict energy constraints guided the development of a new class of so-called light-weight manycore processors. This study evaluates the computing and energy performance of two well-known irregular NP-hard problems-the Traveling-Salesman Problem (TSP) and K-Means clustering-and a numerical seismic wave propagation simulation kernel-Ondes3D-on multicore, NUMA, and manycore platforms. First, we concentrate on the nontrivial task of adapting these applications to a manycore, specifically the novel MPPA-256 manycore processor. Then, we analyze their performance and energy consumption on those different machines. Our results show that applications able to fully use the resources of a manycore can have better performance and may consume from 3.8 Ă to 13 Ă less energy when compared to low-power and general-purpose multicore processors, respectively.763248Andreolli, C., Thierry, P., Borges, L., Yount, C., Skinner, G., Genetic algorithm based auto-tuning of seismic applications on multi and manycore computers (2014) EAGE Workshop on High Performance Computing for Upstream, Amsterdam, Netherlands, , http://dx.doi.org/10.3997/2214-4609.20141920, SeptemberAochi, H., Ulrich, T., Ducellier, A., Dupros, F., Michea, D., Finite difference simulations of seismic wave propagation for understanding earthquake physics and predicting ground motions: Advances and challenges (2013) J. Phys.: Conf. 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Software-Based Hardening Strategies for Neutron Sensitive FFT Algorithms on GPUs
In this paper we assess the neutron sensitivity of Graphics Processing Units (GPUs) when executing a Fast Fourier Transform (FFT) algorithm, and propose specific software-based hardening strategies to reduce its failure rate. Our research is motivated by experimental results with an unhardened FFT that demonstrate a majority of multiple errors in the output in the case of failures, which are caused by data dependencies. In addition, the use of the built-in error-correction code (ECC) showed a large overhead, and proved to be insufficient to provide high reliability. Experimental results with the hardened algorithm show a two orders of magnitude failure rate improvement over the original algorithm (one order of magnitude over ECC) and an overhead 64% smaller than ECC