Dan Iter is a PhD student at Stanford University. He is advised by Professor Dan Jurafsky
and is a member of the NLP Group and AI Lab. He is interested in generative models for text …

Unexpectedly, the rise of serverless computing has also collaterally started the
“democratization” of massive-scale data parallelism. This new trend heralded by PyWren …

In the cloud environment, different kinds of jobs (Flink, PyTorch, TensorFlow, AI-Serving) are
running in the same cluster with different service-level agreements (SLA). To manage large …

Task scheduling and execution over large scale, distributed systems plays an important role
on achieving good performance and high system utilization. Job management systems need …

We present gg: a system for executing interdependent software workflows across thousands
of short-lived “lambdas” that run in parallel on public cloud infrastructure. The system …

Python-written data analytics applications can be modeled as and compiled into a directed
acyclic graph (DAG) based workflow, where the nodes are fine-grained tasks and the edges …

Cloud computing is a new computing paradigm, combining diverse client devices--PCs,
smartphones, sensors, single-function, and embedded--with computation and data storage …

With the ongoing trends in computing and data systems, the time is not far when we will
have exascale systems with millions of nodes and threads of execution being the major …

Abstract Natural Language Processing (NLP) continues to grow in popularity in a range of
research and commercial applications. However, installing, maintaining, and running NLP …

This is the eBook version of the printed book. If the print book includes a CD-ROM, this
content is not included within the eBook version. Google Apps Deciphered Compute in the …