Adaptive Main Memory Compression
Title: Adaptive Main Memory Compression
Irina Chihaia Tuduce and Thomas Gross
Applications that use large data sets frequently exhibit poor performance because the size of their working set exceeds the available physical memory. As a result, these applications suffer from excess page faults and ultimately exhibit thrashing behavior. For some applications, compression offers a way to reduce the number of page faults that must be serviced from the disk. We describe here a system that can be implemented with a small number of kernel changes.
The key idea to exploit the benefits of memory compression is to adapt the allocation of real (physical) memory between uncompressed and compressed pages without user involvement. The system manages its resources dynamically on the basis of the varying demands of each application and also on the situational requirements that are data dependent. The technique used to localize page fragments in the compressed area allows the system to reclaim or add space easily if it is advisable to shrink or grow the size of the compressed area.
The design is implemented in Linux, runs on both 32-bit and 64-bit architectures, and has been demonstrated to work in practice under complex workload conditions and memory pressure. The benefits from our approach depend on the relationship between the size of the compressed area, the application’s compression ratio, and the access pattern of the application. For a range of benchmarks and applications, the system shows an increase in performance by a factor of 1.3 to 55.
Thomas R. Gross is a Professor of Computer Science at ETH Zurich, Switzerland. He is the head of the Computer Systems Institute, from 1999-2004 he was the deputy director of the NCCR on on “Mobile Information and Communication Systems”, a research center funded by the Swiss National Science Foundation. He is also an Adjunct Professor in the School of Computer Science at Carnegie Mellon University.
Thomas Gross joined CMU in 1984 after receiving a Ph.D. in Electrical Engineering from Stanford University. In 2000, he became a Full Professor at ETH Zurich. He is interested in tools, techniques, and abstractions for software construction and has worked on many aspects of the design and implementation of programs. To add some realism to his research, he has focussed on compilers for uni-processors and parallel systems and has contributed to many areas of compilation (code generation, optimization, debugging, partitioning of computations, data parallelism and task parallelism). Compilers are also interesting systems that illustrate the use of many concepts to structure programs (frameworks, patterns, components). Compilers require a good cost-model of the target environment (e.g., to make space-time tradeoffs) but recent systems have become so complex that simple models no longer suffice. In his current research, Thomas Gross and his colleagues investigate network- and system-aware programs — i.e. programs that can adjust their resource demands in response to resource availability.
In addition to working on compilers, Thomas Gross has been involved in several projects that straddle the boundary between applications and compilers. And since many programs are eventually executed on real computers, He has also participated in the past in the development of several machines: the Stanford MIPS processor, the Warp systolic array, and the iWarp parallel systems. His current work in computer systems concentrates on networks.
Date: 2006-May-03 Time: 10:00:00 Room: Sala 905 (Sala Omega do POSI)
For more information:
Research data repositories and tools for human genomics data sharing
Inform the human research community of the status and availability of BioData.pt Local EGA and discuss its need and usability challenges.
The European Genome-phenome Archive (EGA) is a repository for all sequence and genotype experiment types, including case-control, population, and family studies. The EGA will serve as a permanent archive that will archive several levels of data, including the raw data (which could, for example, be re-analysed in the future by other algorithms) as well as the genotype calls provided by the submitters.
Responding to national regulations over human data sharing and other constraints, BioData.pt deploys and operates a Local EGA instance and tools that allow data discovery of genomic and phenoclinic data, following the GA4GH standard and international best practices.
This workshop aims at informing the human research community of the status and availability of BioData.pt Local EGA and discuss from several perspectives its need and usability challenges.
Further details and registration are available here.
OLISSIPO Summer School in Lisbon | Computational phylogenetics to analyse the evolution of cells and communities
We are happy to announce the OLISSIPO Summer School on Computational phylogenetics to analyse the evolution of cells and communities, which will be held in Lisbon, Portugal, at INESC-ID, between July 2-7, 2023.
David Posada, University of Vigo (class)
João Alves, University of Vigo (hands-on)
Nadia El-Mabrouk, Université de Montréal (class)
Mattéo Delabre, Université de Montréal (hands-on)
Ran Libeskind-Hadas, Claremont McKenna College (class and hands-on)
Russell Schwartz, Carnegie Mellon University (class and hands-on)
See the preliminary agenda at: https://olissipo.inesc-id.pt/tree-tango-school
Registration is mandatory. You can register at: https://forms.gle/VsASFHW5E7MJvaCc9
The registration fee is 250€ for students and OLISSIPO members and 350€ for postdocs or other researchers (meals indicated at the schedule of the school are included, accommodation and flights are not). All details will be made available upon registration.
We will have slots for flash talks (3-10 min depending on the number of submissions) to present yourself and the work you have been developing in your research.
The 13th Lisbon Machine Learning School | LxMLS 2023
The Lisbon Machine Learning Summer School (LxMLS) takes place yearly at Instituto Superior Técnico (IST). LxMLS 2023 will be a 6-day event (14-20 July, 2023), scheduled to take place as an in-person event.
The school covers a range of machine learning topics, from theory to practice, that are important in solving natural language processing problems arising in different application areas. It is organized jointly by Instituto Superior Técnico (IST), a leading Engineering and Science school in Portugal, the Instituto de Telecomunicações, the Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC-ID), the Lisbon ELLIS Unit for Learning and Intelligent Systems (LUMLIS), Unbabel, Zendesk, and IBM Research.
Check online for information about past editions: LxMLS 2011, LxMLS 2012, LxMLS 2013, LxMLS 2014, LxMLS 2015, LxMLS 2016, LxMLS 2017, LxMLS 2018, LxMLS 2019, LxMLS 2020, LxMLS 2021, LxMLS 2022 (you can also watch the videos of the lectures for 2016, 2017, 2018, and 2020).
31st International Conference on Information Systems Development (ISD 2023)
The 31st International Conference on Information Systems Development (ISD 2023) conference provides a forum for research and developments in the field of information systems. The theme of ISD 2023 is “Information systems development, organizational aspects and societal trends”. New trends in developing information systems emphasize the continuous collaboration between developers and operators in order to optimize the software delivery time. The conference promotes research on methodological and technological issues and how IS developers and operators are transforming organizations and society through information systems.
The ISD 2023 conference held this year also provides an opportunity for researchers and practitioners to promote their research, practical experience, and to discuss issues related to Information Systems through papers, posters, and journal-first paper presentations.
ISD 2023 will be hosted by Instituto Superior Técnico, in Lisbon, Portugal, on August 30–September 1, 2023.