A Protected Dataplane Operating System for High Throughput and Low Latency

Notary: A Device for Secure Transaction Approval

Ward is a Spectre and Meltdown resistant research operating system based on sv6 which was in turn based on xv6.

AIFM stands for Application-Integrated Far Memory. It provides a simple, general, and high-performance mechanism for users to adapt ordinary memory-intensive applications to far memory. Different from existing paging-based systems, AIFM exposes far memory as far-memory pointers and containers in the language level.

DeepPool is a prototype training system that achieves efficient strong scaling by implementing burst parallel training and GPU multiplexing.

A datacenter network framework that aims for high utilization with zero queueing. A logically centralized arbiter controls and orchestrates all network transfers.

Helium: Lifting High-Performance Stencil Kernels from Stripped x86 Binaries to Halide DSL Code

PetaBricks is a new implicitly parallel language and compiler where having multiple implementations of multiple algorithms to solve a problem is the natural way of programming

Tiramisu is a compiler for expressing fast and portable data parallel computations. It provides a simple C++ API for expressing algorithms (Tiramisu expressions) and how these algorithms should be optimized by the compiler. Tiramisu can be used in areas such as linear and tensor algebra, deep learning, image processing, stencil computations and machine learning.

SynapseML (previously known as MMLSpark), is an open-source library that simplifies the creation of massively scalable machine learning (ML) pipelines. SynapseML provides simple, composable, and distributed APIs for a wide variety of different machine learning tasks such as text analytics, vision, anomaly detection, and many others. SynapseML is built on the Apache Spark distributed computing framework and shares the same API as the SparkML/MLLib library, allowing you to seamlessly embed SynapseML models into existing Apache Spark workflows.