This paper presents a system-level method for achieving the rapid deployment and high-
density caching of serverless functions in a FaaS environment. For reduced start times …

Machine learning is becoming pervasive, decades of research in neural network
computation is now being leveraged to learn patterns in data and perform computations that …

Modern application development addresses increasingly specialized problems using
domain-specific utilities, such as Optical Code Recognition and standalone statistical tools …

In this paper, we present a dynamic compilation system called ExanaDBT for transparently
optimizing and parallelizing binaries at runtime based on the polyhedral model. Starting …

Prior work on Automatically Scalable Computation (ASC) suggests that it is possible to
parallelize sequential computation by building a model of whole-program execution, using …

We present Lerna, an end-to-end tool that automatically and transparently detects and
extracts parallelism from data-dependent sequential loops. Lerna uses speculation …

Neural network accelerators are an increasingly utilized component of heterogeneous
multicore architectures. This new utilization stems from their capability to improve the power …

Chip Multiprocessors (CMP) are everywhere, from mobile systems to servers. Thread-Level
Parallelism (TLP) is the characteristic of a program that makes use of the parallel cores of a …

Any concrete information processing task can be viewed as the sequential manipulation of
information over time, receiving inputs and producing outputs. To accomplish this …

Parallel computer architectures have dominated the computing landscape for the past two
decades; a trend that is only expected to continue and intensify, with increasing …