CC* Compute: GPU Infrastructure to Explore New Algorithmic & AI Methods in Data-Driven Science and Engineering at Tufts University

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Advanced scientific and engineering research at Tufts University is employing increasingly complex models, algorithms, simulations and machine learning approaches to large datasets. The larger the dataset or the more complex the model, the longer it takes to compute, slowing down researchers' progress and limiting their ability to innovate. Tufts' addition of six Graphics Processing Unit (GPU) enhanced compute nodes to its high-performance computing cluster accelerates scientific and engineering research in the areas of Algorithm Parallelization & Acceleration and Machine Learning & Deep Learning. Researchers in biology, chemistry, computer science, mathematics, physics, and urban planning leverage the GPU enhanced infrastructure to develop new algorithms and models and accelerate scientific discoveries. Through collaboration with the NSF-funded T-TRIPODS (Transdisciplinary Research in Principles of Data Science) project and the Center for STEM diversity at Tufts, the infrastructure provides new opportunities for underrepresented students to acquire and extend data science and high-performance computing skills.The six GPU enhanced compute nodes are each configured with dual 20-core Intel Xeon Gold 6248 CPUs, 768GB of RAM and 8 NVIDIA Tesla V100 (32GB) GPUs interconnected with NVLink to improve scaling of multi-GPU computation. The nodes are linked with a 100 gigabit network and are accessible to researchers at Tufts and externally through the Open Science Grid (OSG). The large core count, large RAM, interlinked GPU architecture provides the greatest flexibility for researchers to mix traditional and GPU-enhanced approaches in complex computational analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.