GPU-accelerated Chemical Similarity Assessment for Large Scale Databases

The assessment of chemical similarity between molecules is a basic operation in chemoinformatics, a computational area concerning with the manipulation of chemical structural information. Comparing molecules is the basis for a wide range of applications such as searching in chemical databases, training prediction models for virtual screening or aggregating clusters of similar compounds. However, currently available multimillion databases represent a challenge for conventional chemoinformatics algorithms raising the necessity for faster similarity methods. In this paper, we extensively analyze the advantages of using many-core architectures for calculating some commonly-used chemical similarity coe_cients such as Tanimoto, Dice or Cosine. Our aim is to provide a wide-breath proof-of-concept regarding the usefulness of GPU architectures to chemoinformatics, a class of computing problems still uncovered. In our work, we present a general GPU algorithm for all-to-all chemical comparisons considering both binary fingerprints and floating point descriptors as molecule representation. Subsequently, we adopt optimization techniques to minimize global memory accesses and to further improve e_ciency. We test the proposed algorithm on different experimental setups, a laptop with a low-end GPU and a desktop with a more performant GPU. In the former case, we obtain a 4-to-6-fold speed-up over a single-core implementation for fingerprints and a 4-to-7-fold speed-up for descriptors. In the latter case, we respectively obtain a 195-to-206-fold speed-up and a 100-to-328-fold speed-up.National Institutes of Health (U.S.) (grant GM079804)National Institutes of Health (U.S.) (grant GM086145

BEBOP: Bidirectional dEep Brain cOnnectivity maPping

Functional connectivity mapping provides information about correlated brain areas, useful for many applications such as on mental disorders. This work aims to improve this mapping by using deep metric learning considering the directionality of information flow and time-domain features. To deal with the computational cost of a complete pairwise combination network, we trained a network able to recognize similar signals and, after training, feed it with all combinations of signals from each brain area. The labels of similarity or dissimilarity are determined by agglomerative clustering using the Jensen-Shannon Distance as a metric. To validate our approach we employed a resting-state eye-open functional MRI dataset from ADHD and healthy subjects. Once registered, the signals are filtered and averaged by area with a functional trimmed mean. After obtaining the connectivity maps from each subject, we perform a feature importance selection using logistic regression. The ten most promising areas were extracted, such as the frontal cortex and the limbic system. These results are in complete agreement with previous literature. It is well known those areas are mainly involved in attention and impulsivity

PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems

Machine Learning models are often composed of pipelines of transformations. While this design allows to efficiently execute single model components at training time, prediction serving has different requirements such as low latency, high throughput and graceful performance degradation under heavy load. Current prediction serving systems consider models as black boxes, whereby prediction-time-specific optimizations are ignored in favor of ease of deployment. In this paper, we present PRETZEL, a prediction serving system introducing a novel white box architecture enabling both end-to-end and multi-model optimizations. Using production-like model pipelines, our experiments show that PRETZEL is able to introduce performance improvements over different dimensions; compared to state-of-the-art approaches PRETZEL is on average able to reduce 99th percentile latency by 5.5x while reducing memory footprint by 25x, and increasing throughput by 4.7x.Comment: 16 pages, 14 figures, 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 201