Meetings
- FZ workshop in Sarasota, FL
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Thank you for considering attending the FZ/ZF Joint workshop at Virginia Tech in Alexandria, Virginia!
Dates, address, contact:
Date: October 1-3, 2025. 9:00 AM to 5:00 PM
Address: E Glebe Rd, Alexandria, VA 22305
Host: Dr. Lingja Liu (ljliu@vt.edu)
Google Form (food, restrictions): register for the workshop.
Google Form (proposed presentations): submit your talk
some people might arrive early and have dinner together
Where should I fly?: if you need to fly Washington Reagan National Airport (DCA) is about 5 minute metro ride on the blue/yellow line towards Franconia/Huntington to Potomac Yard station, with Washington Dulles (IAD) is about an hour away on the metro (<7$) take the sliver line from Dulles towards Largo/NewCarlton to Roslyn, then change trains to the blue line to Franconia. You can pay for the metro with your credit card and do not need to obtain a separate metro card. The metro in DC is relatively safe and is often faster than a car.
Where to stay? Hotel/accommodation information: you can find hotel discount codes provided by VT attached. If applicable, you can also use the Gov’t rate
What is changing in this edition?: Compared to Spring, the consensus of both teams is that it would be helpful to have more time for discussion between the projects. To facilitate that, it was proposed that some talks that were given as plenary in the last workshop be given as a poster session or as breakout sessions so discussion can happen in parallel and reduce the number of plenary talks. Additionally, it was suggested that as a general rule, because FZ plenary talks went on day one at the Spring meeting, the ZF team plenary talks would go on day 1 in the Fall meeting. The exact choice of breakout/plenary/posters will be made based on expressed interest, time, and space availability.
Remote Participation? We’ll do our best to accommodate remote participation, but in-person attendance is strongly preferred for active members of the project. External collaborators who attend remotely may do so at no cost.
Thank you for considering attending the FZ ZF Joint workshop at Sarasota!
Dates, address, contact:
Schedule: The up-to-date schedule can be found here
Hotels:
March is a very busy season in Sarasota as it is a popular spring break destination. Please book your hotel as soon as possible. We recommend staying at hotels that are within walking distance of the venue:
Transportation:
Photo:
Thank you for attending the FZ workshop at Columbus!
Thank you for considering attending the FZ workshop at Sarasota!
Dates, address, contact:
Schedule:
Day before (Feb. 13)
Day 1 (Feb 14)
Day 2 (Feb 15)
Photos:
Thank you for considering attending the FZ Kickoff Meeting!
All slides for talks in the meeting can be found in this shared folder.
Here is the schedule:
The ISC Tutorials are interactive courses focusing on key topics of high performance computing, networking, storage, and data science. Renowned experts in their respective fields will give attendees a comprehensive introduction to the topic as well as providing a closer look at specific problems. Tutorials are encouraged to include a “hands-on” component to allow attendees to practice prepared materials.
The Tutorials will be held on Thursday, June 24, and on Friday, June 25, 2021.
The ISC 2021 Tutorials Committee is headed by Kevin Huck, University of Oregon, USA, with Kathryn Mohror, Lawrence Livermore National Laboratory, USA, as Deputy Chair.
Today’s modern applications are producing too large volumes of data to be stored, processed, or transferred efficiently. Data reduction is becoming an indispensable technique in many domains because it can offer a great capability to reduce the data size by one or even two orders of magnitude, significantly saving the memory/storage space, mitigating the I/O burden, reducing communication time, and improving the energy/power efficiency in various parallel and distributed environments, such as high-performance computing (HPC), cloud computing, edge computing, and Internet-of-Things (IoT). An HPC system, for instance, is expected to have a computational capability of floating-point operations per second, and large-scale HPC scientific applications may generate vast volumes of data (several orders of magnitude larger than the available storage space) for post-anlaysis. Moreover, runtime memory footprint and communication could be non-negligible bottlenecks of current HPC systems.
Tackling the big data reduction research requires expertise from computer science, mathematics, and application domains to study the problem holistically, and develop solutions and harden software tools that can be used by production applications. Specifically, the big-data computing community needs to understand a clear yet complex relationship between application design, data analysis and reduction methods, programming models, system software, hardware, and other elements of a next-generation large-scale computing infrastructure, especially given constraints on applicability, fidelity, performance portability, and energy efficiency. New data reduction techniques also need to be explored and developed continuously to suit emerging applications and diverse use cases.
There are at least three significant research topics that the community is striving to answer: (1) whether several orders of magnitude of data reduction is possible for extreme-scale sciences; (2) understanding the trade-off between the performance and accuracy of data reduction; and (3) solutions to effectively reduce data size while preserving the information inside the big datasets.
The goal of this workshop is to provide a focused venue for researchers in all aspects of data reduction in all related communities to present their research results, exchange ideas, identify new research directions, and foster new collaborations within the community.
More information can be found [here]
Compression for scientific data
Compression for scientific data
Large-scale numerical simulations and experiments are generating very large datasets that are difficult to analyze, store and transfer. This problem will be exacerbated for future generations of systems. Data reduction becomes a necessity in order to reduce as much as possible the time lost in data transfer and storage. Lossless and lossy data compression are attractive and efficient techniques to significantly reduce data sets while being rather agnostic to the application. This tutorial will review the state of the art in lossless and lossy compression of scientific data sets, discuss in detail two lossy compressors (SZ and ZFP) and introduce compression error assessment metrics. The tutorial will also cover the characterization of data sets with respect to compression and introduce Z-checker, a tool to assess compression error.
More specifically the tutorial will introduce motivating examples as well as basic compression techniques, cover the role of Shannon Entropy, the different types of advanced data transformation, prediction and quantization techniques, as well as some of the more popular coding techniques. The tutorial will use examples of real world compressors (GZIP, JPEG, FPZIP, SZ, ZFP, etc.) and data sets coming from simulations and instruments to illustrate the different compression techniques and their performance. This 1/2 day tutorial is improved from the evaluations of the two highly attended and rated tutorials given on this topic at ISC17 and SC17.
DescriptionLarge-scale numerical simulations, observations and experiments are generating very large datasets that are difficult to analyze, store and transfer. Data compression is an attractive and efficient technique to significantly reduce the size of scientific datasets. This tutorial reviews the state of the art in lossy compression of scientific datasets, discusses in detail two lossy compressors (SZ and ZFP), introduces compression error assessment metrics and the Z-checker tool to analyze the difference between initial and decompressed datasets. The tutorial will offer hands-on exercises using SZ and ZFP as well as Z-checker. The tutorial addresses the following questions: Why lossless and lossy compression? How does compression work? How measure and control compression error? The tutorial uses examples of real-world compressors and scientific datasets to illustrate the different compression techniques and their performance. Participants will also have the opportunity to learn how to use SZ, ZFP and Z-checker for their own datasets. The tutorial is given by two of the leading teams in this domain and targets primarily beginners interested in learning about lossy compression for scientific data. This half-day tutorial is improved from the evaluations of the highly rated tutorials given on this topic at ISC17, SC17 and SC18.
Large-scale numerical simulations, observations and experiments are generating very large datasets that are difficult to analyze, store and transfer. This problem will be exacerbated for future generations of systems. Data compression is an attractive and efficient technique to significantly reduce the size of scientific datasets while being rather agnostic to the applications. This tutorial reviews the state of the art in lossless and lossy compression of scientific datasets, discusses in detail one lossless (FPZIP) and two lossy compressors (SZ and ZFP), introduces compression error assessment metrics and offers a hands on session allowing participants to use SZ, FPZIP and ZFP as well as Z-checker, a tool to comprehensively assess the compression error. The tutorial addresses the following questions: Why compression, and in particular lossy compression? How does compression work? How measure and control the compression error? What is under the hood of some of the best compressors for scientific datasets? The tutorial uses examples of real world compressors and scientific datasets to illustrate the different compression techniques and their performance. The tutorial is given by two of the leading teams in this domain and targets an audience of beginners and advanced researchers and practitioners in scientific computing and data analytics.
Content Level: 60% beginner, 30% intermediate, 10% advanced
Targeted Audience: This tutorial is for researchers, students and users of high performance computing interested in lossy compression techniques to reduce the size of their datasets: Researchers and students involved in research using or developing new data reduction techniques ; Users of scientific simulations and instruments who require significant data reduction.
Prerequisites: Participants are supposed to bring their own laptop, running Linux or MAC OS X. No previous knowledge in compression or programming language is needed.