Exact(6)
Increasingly, high-performance computing is looking towards data-parallel computational devices to enhance computational performance.
Therefore, an integration of sampling approach and decomposition technique is applied to enhance computational performance.
Active use of mixed-precision floating-point operation is one possible approach to further enhance computational performance.
To enhance computational performance, many instruction level parallelism techniques have been implemented in current microprocessors, e.g., data forwarding, out-of-order execution, register renaming etc.
In order to enhance computational performance, we compress expression profiles by grouping reads into blocks.
With the rapid development of multicore hardware, graphics processing units (GPUs) are being used in numerous applications to enhance computational performance.
Similar(54)
Parallel implementations are widely used in digital architectures to enhance computational performances, exploiting the number of involved processing units.
This study of testing the performance characteristics of supercomputers with advanced computational algorithms that offer optimal trade-off to achieve enhanced computational performance serves to demonstrate that such simulations are feasible with currently available HPC resources.
Two heuristic operators are proposed to enhance its computational performance.
This chapter describes some variants of this general method that incorporate several specialized techniques to enhance the computational performance.
The Graphics Processing Unit (GPU) is such a platform that provides an opportunity to significantly enhance the computational performance and thus gain new insight into this problem.
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