GPGPU

Recent works have shown that there exist microarchitectural timing channels in contemporary GPUs, which make table-based cryptographic algorithms like AES vulnerable to side channel timing attacks. Also, table-based cryptographic algorithms have been ...

Deep neural networks (DNNs) have been proving the effectiveness in various computing fields. To provide more efficient computing platforms for DNN applications, it is essential to have evaluation environments that include assorted benchmark workloads. ...

GPUs seem to be ideal for algorithms that work in parallel. A number of ways to represent graphs in GPU memory are known. But so far there are no guidelines to select the representation that is likely to result in the best performance.

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K Nearest Neighbor (KNN) joins are used in many scientific domains for data analysis, and are building blocks of several well-known algorithms. KNN-joins find the KNN of all points in a dataset. However, KNN searches are computationally expensive, and ...

The CUDA Unified Memory (UM) interface enables a significantly simpler programming paradigm and has the potential to fundamentally change the way programmers write CUDA applications in the future. Although UM leads to high productivity in programming ...

While GPU Computing is pervasive in various areas, including scientific-technical computing and machine learning, single GPUs are often insufficient to meet application demand. Furthermore, multi-GPU processing is a promising option to achieve a ...