The workshop on advances in software and hardware for big data to knowledge discovery aims to bridge latest technology development in hardware and software with end users of big data. The workshop will discuss issues that concern domain researchers about the latest software and hardware technologies, such as performance evaluation, optimizations, accessibility and usability of new technologies. We would like to invite cyber-infrastructure specialists and computer scientist to share their experience with latest hardware and software advancements as well as data scientists to share their experiences and perspectives in using those technologies for data driven discovery. Fueled by the big data analytics needs, new computing and storage technologies are developing rapidly and pushing for new high-end hardware geared toward big data problems. While those technologies have the potential to greatly improve effectiveness of big data analytics, the cost and sophistications of those technology and limited initial application support often make them inaccessible to the end users and not fully utilized in academia years later. Meanwhile, comprehensive analytic software environment and platform, such as R and Python, have become increasingly popular open-source platform for data analysis. Those software not only provides collection of analytic methods but also has the potential to utilize new hardware transparently and ease the efforts required from the end users. However, most data scientists have only had experience with small to medium-sized data; and now the size of Big Data poses its own challenges. It is therefore a critical issue to make the latest technology advancements in software and hardware accessible and usable to the domain scientists in a timely manner, especially those in fields traditionally not strong in computation and programming. The topics of the workshop are centered on the accessibility and applicability of the latest hardware and software to practical domain problems. Topics of interest include, but are not limited to: * Adopting hardware technology, such as GPGPU, Xeon Phi etc, for Big Data analytics * Application and use cases in using cyber-infrastructure for Big Data in sciences and engineering * Big data and interactive analysis languages (e.g., R, Python, and Matlab) * New advances in hardware technology. * Novel software platforms and models for big data collection management and analysis. * Performance tuning with new hardware infrastructure and software platform * Search and data retrieval on large-scale data set * Service oriented architectures to enable data science * Software and platform for big data analysis and visualization. Important dates: Aug 30, 2014: Due date for papers submission Sept 20, 2014: Notification of paper acceptance to authors Oct 5, 2014: Camera-ready of accepted papers October 27-30 2014: Workshops For more information and submission instructions please visit https://www.tacc.utexas.edu/news/events/2014/ieee/software-hardware-big-data.