Thread‐level parallelism (TLP) has been widely exploited to optimize computational resource usage in high‐performance systems. However, as many applications do not scale as the number of threads increase, resources will be wasted when the application executes with the maximum possible number of threads (i.e., the default execution) rather than fewer threads (thread throttling) that may use the resources more efficiently. Hence, instead of executing only one application with as many threads as possible, one can run more applications simultaneously by applying thread throttling to each one. The primary outcome of this strategy is a significant reduction in the total execution time and energy consumption when the system needs to execute a list of applications. Given that, we propose a smart resource allocation (SRA) for concurrent parallel application execution. It automatically finds the ideal degree of TLP for each application and guides the simultaneous parallel applications execution. When running 25 well‐known benchmarks on three multicore systems and comparing SRA to state‐of‐the‐art strategies (e.g., Batch, Equal policy, and Scalability), SRA improves the EDP by 87.4% over the Batch strategy; 75.5% over the Equal policy; and 38.8% over the scalability strategy.
Several aspects limit the scalability of parallel applications, e.g., off-chip bus saturation and data synchronization. Moreover, the high cost of cooling HPC systems, which can outweigh the cost of developing the system itself, has pushed the parallel application’s execution to another level of requirements, in terms of performance and energy. In this work, we propose AtTune: a heuristic-based framework for tuning the number of processes/threads and CPU frequency to optimize the parallel applications’ execution. AtTune is transparent for the user, independent of the input size, and it optimizes for different parallel programming models. We evaluated our proposed solution considering five well-known kernels implemented in MPI and OpenMP. Experimental results on two real multi-core systems showed that AtTune improves up to 36%, 11%, and 32% the energy efficiency, performance, and Energy-Delay Product, respectively.
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