A comprehensive survey on coded distributed computing: Fundamentals, challenges, and networking applications
Distributed computing has become a common approach for large-scale computation tasks
due to benefits such as high reliability, scalability, computation speed, and cost …
due to benefits such as high reliability, scalability, computation speed, and cost …
Ray: A distributed framework for emerging {AI} applications
The next generation of AI applications will continuously interact with the environment and
learn from these interactions. These applications impose new and demanding systems …
learn from these interactions. These applications impose new and demanding systems …
Faasm: Lightweight isolation for efficient stateful serverless computing
S Shillaker, P Pietzuch - … Annual Technical Conference (USENIX ATC 20 …, 2020 - usenix.org
Serverless computing is an excellent fit for big data processing because it can scale quickly
and cheaply to thousands of parallel functions. Existing serverless platforms isolate …
and cheaply to thousands of parallel functions. Existing serverless platforms isolate …
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
TensorFlow is an interface for expressing machine learning algorithms, and an
implementation for executing such algorithms. A computation expressed using TensorFlow …
implementation for executing such algorithms. A computation expressed using TensorFlow …
[HTML][HTML] {SONIC}: Application-aware data passing for chained serverless applications
The conference papers and full proceedings are available to registered attendees now and
will be available to everyone beginning Wednesday, July 14, 2021. Paper abstracts and …
will be available to everyone beginning Wednesday, July 14, 2021. Paper abstracts and …
Big data: A survey
In this paper, we review the background and state-of-the-art of big data. We first introduce
the general background of big data and review related technologies, such as could …
the general background of big data and review related technologies, such as could …
Visualizing dataflow graphs of deep learning models in tensorflow
K Wongsuphasawat, D Smilkov… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow
machine intelligence platform. This tool helps users understand complex machine learning …
machine intelligence platform. This tool helps users understand complex machine learning …
Toward scalable systems for big data analytics: A technology tutorial
Recent technological advancements have led to a deluge of data from distinctive domains
(eg, health care and scientific sensors, user-generated data, Internet and financial …
(eg, health care and scientific sensors, user-generated data, Internet and financial …
Naiad: a timely dataflow system
Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers
the high throughput of batch processors, the low latency of stream processors, and the ability …
the high throughput of batch processors, the low latency of stream processors, and the ability …
Resilient distributed datasets: A {Fault-Tolerant} abstraction for {In-Memory} cluster computing
We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets
programmers perform in-memory computations on large clusters in a fault-tolerant manner …
programmers perform in-memory computations on large clusters in a fault-tolerant manner …