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Convolutional Neural Networks have dramatically improved in recent years, surpassing human accuracy on certain problems and performance exceeding that of traditional computer vision algorithms. While the compute pattern in itself is relatively simple, significant compute and memory challenges remain as CNNs may contain millions of floating-point parameters and require billions of floating-point operations...
Smith-Waterman is a dynamic programming algorithm that plays a key role in the modern genomics pipeline as it is guaranteed to find the optimal local alignment between two strings of data. The state of the art presents many hardware acceleration solutions that have been implemented in order to exploit the high degree of parallelism available in this algorithm. The majority of these implementations...
The goal of reaching exascale computing is made especially challenging by the highly heterogeneous nature of modern platforms and the energy they consume. As compute nodes typically utilize multiple multi-core CPU and are increasingly equipped with PCIe based accelerators, both are contributing to an ever more dynamic power consumption. In our study we evaluate our target application on a variety...
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