11.10.2021

Three Papers accepted at HPEC and ComplexNetworks

The IEEE conference on High Performance and Embedded Computing (HPEC) deals with computing hardware, software, systems and applications where performance matters. The Algorithm Engineering group is proud to contribute two papers to this years venue, taking place virtually on September 20 - 24, 2021. Both papers result from fruitful collaborations with the LISP group from Boston University and with the IBM Research Almaden group. In particular, the authors use a new Python library PyMM which allows using existing Python data-types, such as Numpy and PyTorch tensor, on non-volatile memory and show the potential to accelerate machine learning posterior estimation and dimensionality reduction on non-volatile memory.

Furthermore, the Algorithm Engineering group contributes another paper to the 10th International Conference on Complex Networks and their Applications (ComplexNetworks) in Madrid, Spain, on November 30 - December 2, 2021. There, Otto Kißig and Martin Taraz continue their work on drug repurposing published in the ICLR Workshop on Machine Learning for Preventing and Combating Pandemics 2021 (MLPCP@ICLR) and later in the ICML Workshop on Computational Biology 2021 (WCB@ICML). The work was then extended in collaboration with the Boston Universtiy and IBM to run these algorithms using NVRAM and show that NVRAM can accelerate drug-repuposing computations. This is the first conference paper that contains the results of the above workshops.

Clean Citation Style 005

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Wood, Andrew; Hershcovitch, Moshik; Waddington, Daniel; Cohen, Sarel; Wolf, Meredith; Suh, Hongjun; Zong, Weiyu; Chin, Peter Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution AnalysisHigh Performance and Embedded Computing (HPEC) 2021: 1–7

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Wood, Andrew; Hershcovitch, Moshik; Waddington, Daniel; Cohen, Sarel; Chin, Peter Non-Volatile Memory Accelerated Posterior EstimationHigh Performance and Embedded Computing (HPEC) 2021

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Clean Citation Style 005

Cohen, Sarel; Hershcovitch, Moshik; Taraz, Martin; Kißig, Otto; Wood, Andrew; Waddington, Daniel; Chin, Peter; Friedrich, Tobias Drug Repurposing using Link Prediction on Knowledge Graphs with Applications to Non-Volatile MemoryComplex Networks and their Applications (ComplexNetworks) 2021: 742–753

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@inproceedings{cohen2021repurposing,
  abstract = {The active global SARS-CoV-2 pandemic caused more than 167 million cases and 3.4 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time intensive process and despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of \emph{drug repurposing}, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on \emph{knowledge graphs}, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi et al. recently developed the Dr-COVID model. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of candidate drugs, 32 of which currently being in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the Dr-COVID model by significantly shortening the inference and pre-processing time by exploiting data-parallelism. As drug repurposing is a task that requires high computation and memory resources, we further accelerate the post-processing phase using a new emerging hardware &mdash; we propose a new approach to leverage the use of high-capacity Non-Volatile Memory for aggregate drug ranking.},
  author = {Cohen, Sarel and Hershcovitch, Moshik and Taraz, Martin and Kißig, Otto and Wood, Andrew and Waddington, Daniel and Chin, Peter and Friedrich, Tobias},
  booktitle = {Complex Networks and their Applications (ComplexNetworks)},
  keywords = {sarelcohen ottokissig danielwaddington moshikhershcovitch tobiasfriedrich martintaraz andrewwood complexnetworks peterchin year2021},
  pages = {742-753},
  title = {Drug Repurposing using Link Prediction on Knowledge Graphs with Applications to Non-Volatile Memory},
  year = 2021
}