A Templated C++ Interface for ISL

Polyhedral libraries typically support only a very limited collection of types for representing objects, corresponding to broad mathematical classes such as sets, binary relations and functions.

Fast Stencil-Code Computation on a Wafer-Scale Processor

The performance of CPU-based and GPU-based systems is often low for PDE codes, where large, sparse, and often structured systems of linear equations must be solved. Iterative solvers are limited by data movement, both between caches and memory and between nodes.

Epigenomic language models powered by Cerebras

Large scale self-supervised pre-training of Transformer language models has advanced the field of Natural Language Processing and shown promise in cross-application to the biological `languages' of proteins and DNA. Learning effective representations of DNA sequences using large genomic sequence corpuses may accelerate the development of models of gene regulation and function through transfer learning. However, to accurately model cell type-specific gene regulation and function, it is necessary…

Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical com- pounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide suffciently high resolution or timescale to capture important dynamics of this molecular…

Stream-AI-MD: streaming AI-driven adaptive molecular simulations for heterogeneous computing platforms

Emerging hardware tailored for artificial intelligence (AI) and machine learning (ML) methods provide novel means to couple them with traditional high performance computing (HPC) workflows involving molecular dynamics (MD) simulations. We propose Stream-AI-MD, a novel instance of applying deep learning methods to drive adaptive MD simulation campaigns in a streaming manner.

Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation

We propose combining memory saving techniques with traditional U-Net architectures to increase the complexity of the models on the Brain Tumor Segmentation (BraTS) challenge. The BraTS challenge consists of a 3D segmentation of a 240 240 155 4 input image into a set of tumor classes.

Pipelined Backpropagation at Scale: Training Large Models without Batches

New hardware can substantially increase the speed and efficiency of deep neural network training. To guide the development of future hardware architectures, it is pertinent to explore the hardware and machine learning properties of alternative training algorithms.

System Integration of Neocortex, a Unique, Scalable AI Platform

The Pittsburgh Supercomputing Center, in partnership with Cerebras Systems and Hewlett Packard Enterprise, has deployed Neocortex, an innovative computing platform that accelerates scientific discovery by vastly shortening the time required for deep learning training and fosters greater integration of deep AI models with scientific workflows.

The curious case of developmental BERTology: On sparsity, transfer learning, generalization and the brain

In this essay, we explore a point of intersection between deep learning and neuroscience, through the lens of large language models, transfer learning and network compression.

Generating SIMD Instructions for Cerebras CS-1 using Polyhedral Compilation Techniques

The Cerebras CS-1 is a computing system based on a waferscale processor having nearly 400,000 compute cores. It is intended for training of and inference on deep neural networks.