Logo: to the web site of Uppsala University

uu.sePublications from Uppsala University
Change search
Refine search result
1 - 11 of 11
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Holmgren, Sverker
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Nordén, Markus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Rantakokko, Jarmo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Wallin, Dan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Performance of PDE solvers on a self-optimizing NUMA architecture2002In: Parallel Algorithms and Applications, ISSN 1063-7192, E-ISSN 1029-032X, Vol. 17, p. 285-299Article in journal (Refereed)
  • 2.
    Löf, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Nordén, Markus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Improving Geographical Locality of Data for Shared Memory Implementations of PDE Solvers2004In: Computational Science – ICCS 2004, Berlin: Springer-Verlag , 2004, p. 9-16Conference paper (Refereed)
  • 3.
    Löf, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Nordén, Markus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Improving geographical locality of data for shared memory implementations of PDE solvers2004Report (Other academic)
    Abstract [en]

    On cc-NUMA multi-processors, the non-uniformity of main memory latencies motivates the need for co-location of threads and data. We call this special form of data locality, geographical locality, as the non-uniformity is a consequence of the physical distance between the cc-NUMA nodes. In this article, we compare the well established method of exploiting the first-touch strategy using parallel initialization of data to an application-initiated page migration strategy as means of increasing the geographical locality for a set of important scientific applications.

    Four PDE solvers parallelized using OpenMP are studied; two standard NAS NPB3.0-OMP benchmarks and two kernels from industrial applications. The solvers employ both structured and unstructured computational grids. The main conclusions of the study are: (1) that geographical locality is important for the performance of the applications, (2) that application-initiated migration outperforms the first-touch scheme in almost all cases, and in some cases even results in performance which is close to what is obtained if all threads and data are allocated on a single node. We also suggest that such an application-initiated migration could be made fully transparent by letting the OpenMP compiler invoke it automatically.

    Download full text (pdf)
    fulltext
  • 4.
    Nordén, Markus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Multithreaded PDE Solvers on Non-Uniform Memory Architectures2006Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    A trend in parallel computer architecture is that systems with a large shared memory are becoming more and more popular. A shared memory system can be either a uniform memory architecture (UMA) or a cache coherent non-uniform memory architecture (cc-NUMA).

    In the present thesis, the performance of parallel PDE solvers on cc-NUMA computers is studied. In particular, we consider the shared namespace programming model, represented by OpenMP. Since the main memory is physically, or geographically distributed over several multi-processor nodes, the latency for local memory accesses is smaller than for remote accesses. Therefore, the geographical locality of the data becomes important.

    The focus of the present thesis is to study multithreaded PDE solvers on cc-NUMA systems, in particular their memory access pattern with respect to geographical locality. The questions posed are: (1) How large is the influence on performance of the non-uniformity of the memory system? (2) How should a program be written in order to reduce this influence? (3) Is it possible to introduce optimizations in the computer system for this purpose?

    The main conclusion is that geographical locality is important for performance on cc-NUMA systems. This is shown experimentally for a broad range of PDE solvers as well as theoretically using a model involving characteristics of computer systems and applications.

    Geographical locality can be achieved through migration directives that are inserted by the programmer or — possibly in the future — automatically by the compiler. On some systems, it can also be accomplished by means of transparent, hardware initiated migration and replication. However, a necessary condition that must be fulfilled if migration is to be effective is that the memory access pattern must not be "speckled", i.e. as few threads as possible shall make accesses to each memory page.

    We also conclude that OpenMP is competitive with MPI on cc-NUMA systems if care is taken to get a favourable data distribution.

    List of papers
    1. OpenMP versus MPI for PDE solvers based on regular sparse numerical operators
    Open this publication in new window or tab >>OpenMP versus MPI for PDE solvers based on regular sparse numerical operators
    2006 (English)In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 22, p. 194-203Article in journal (Refereed) Published
    National Category
    Software Engineering Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-47210 (URN)10.1016/j.future.2003.09.004 (DOI)000234408800016 ()
    Available from: 2006-05-23 Created: 2006-05-23 Last updated: 2018-01-11Bibliographically approved
    2. Performance of PDE solvers on a self-optimizing NUMA architecture
    Open this publication in new window or tab >>Performance of PDE solvers on a self-optimizing NUMA architecture
    2002 (English)In: Parallel Algorithms and Applications, ISSN 1063-7192, E-ISSN 1029-032X, Vol. 17, p. 285-299Article in journal (Refereed) Published
    National Category
    Computer Sciences Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-66909 (URN)10.1080/01495730208941445 (DOI)
    Available from: 2006-05-22 Created: 2006-05-22 Last updated: 2018-01-10Bibliographically approved
    3. Improving Geographical Locality of Data for Shared Memory Implementations of PDE Solvers
    Open this publication in new window or tab >>Improving Geographical Locality of Data for Shared Memory Implementations of PDE Solvers
    2004 (English)In: Computational Science – ICCS 2004, Berlin: Springer-Verlag , 2004, p. 9-16Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Berlin: Springer-Verlag, 2004
    Series
    Lecture Notes in Computer Science ; 3037
    National Category
    Computer Sciences Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-71098 (URN)10.1007/b97988 (DOI)
    Available from: 2007-03-11 Created: 2007-03-11 Last updated: 2018-01-10Bibliographically approved
    4. Geographical locality and dynamic data migration for OpenMP implementations of adaptive PDE solvers
    Open this publication in new window or tab >>Geographical locality and dynamic data migration for OpenMP implementations of adaptive PDE solvers
    2008 (English)In: OpenMP Shared Memory Parallel Programming, Berlin: Springer-Verlag , 2008, p. 382-393Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Berlin: Springer-Verlag, 2008
    Series
    Lecture Notes in Computer Science ; 4315
    National Category
    Computer Sciences Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-17844 (URN)10.1007/978-3-540-68555-5_31 (DOI)000256573200031 ()978-3-540-68554-8 (ISBN)
    Projects
    UPMARC
    Available from: 2008-09-05 Created: 2008-09-05 Last updated: 2022-01-28Bibliographically approved
    5. Performance modelling for parallel PDE solvers on NUMA-systems
    Open this publication in new window or tab >>Performance modelling for parallel PDE solvers on NUMA-systems
    2006 (English)Report (Other academic)
    Abstract [en]

    A detailed model of the memory performance of a PDE solver running on a NUMA-system is set up. Due to the complexity of modern computers, such a detailed model inevitably is very complicated. Therefore, approximations are introduced that simplify the model and allows NUMA-systems and PDE solvers to be described conveniently.

    Using the simplified model, it is shown that PDE solvers using ordered local methods can be made very unsensitive to high NUMA-ratios, allowing them to scale well on virtually any NUMA-system.

    PDE solvers using unordered local methods, semiglobal methods or global methods are more sensitive to high NUMA-ratios and require special techniques in order to scale well beyond a single locality group.

    Nevertheless, the potential performance gain of improving the data distribution on a NUMA-system can be considerable for all kinds of PDE solvers studied.

    Series
    Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2006-041
    National Category
    Computer Sciences Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-81930 (URN)
    Available from: 2008-02-19 Created: 2008-02-19 Last updated: 2024-05-31Bibliographically approved
    Download full text (pdf)
    FULLTEXT01
  • 5.
    Nordén, Markus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Parallel PDE Solvers on cc-NUMA Systems2004Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The current trend in parallel computers is that systems with a large shared memory are becoming more and more popular. A shared memory system can be either a uniform memory architecture (UMA) or a cache coherent non-uniform memory architecture (cc-NUMA).

    In the present thesis, the performance of parallel PDE solvers on cc-NUMA computers is studied. In particular, we consider the shared namespace programming model, represented by OpenMP. Since the main memory is physically, or geographically distributed over several multi-processor nodes, the latency for local memory accesses is smaller than for remote accesses. Therefore, the geographical locality of the data becomes important.

    The questions posed in this thesis are: (1) How large is the influence on performance of the non-uniformity of the memory system? (2) How should a program be written in order to reduce this influence? (3) Is it possible to introduce optimizations in the computer system for this purpose?

    Most of the application codes studied address the Euler equations using a finite difference method and a finite volume method respectively and are parallelized with OpenMP. Comparisons are made with an alternative implementation using MPI and with PDE solvers implemented with OpenMP that solve other equations using different numerical methods.

    The main conclusion is that geographical locality is important for performance on cc-NUMA systems. This can be achieved through self optimization provided in the system or through migrate-on-next-touch directives that could be inserted automatically by the compiler.

    We also conclude that OpenMP is competitive with MPI on cc-NUMA systems if care is taken to get a favourable data distribution.

    Download full text (ps)
    fulltext
  • 6.
    Nordén, Markus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Performance modelling for parallel PDE solvers on NUMA-systems2006Report (Other academic)
    Abstract [en]

    A detailed model of the memory performance of a PDE solver running on a NUMA-system is set up. Due to the complexity of modern computers, such a detailed model inevitably is very complicated. Therefore, approximations are introduced that simplify the model and allows NUMA-systems and PDE solvers to be described conveniently.

    Using the simplified model, it is shown that PDE solvers using ordered local methods can be made very unsensitive to high NUMA-ratios, allowing them to scale well on virtually any NUMA-system.

    PDE solvers using unordered local methods, semiglobal methods or global methods are more sensitive to high NUMA-ratios and require special techniques in order to scale well beyond a single locality group.

    Nevertheless, the potential performance gain of improving the data distribution on a NUMA-system can be considerable for all kinds of PDE solvers studied.

    Download full text (pdf)
    fulltext
  • 7.
    Nordén, Markus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Thuné, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    OpenMP versus MPI for PDE solvers based on regular sparse numerical operators2002In: Computational Science – ICCS 2002, Berlin: Springer-Verlag , 2002, p. 681-690Conference paper (Other academic)
  • 8.
    Nordén, Markus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Thuné, Michael
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    OpenMP versus MPI for PDE solvers based on regular sparse numerical operators2006In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 22, p. 194-203Article in journal (Refereed)
  • 9.
    Nordén, Markus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Löf, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Rantakokko, Jarmo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Dynamic data migration for structured AMR solvers2007In: International journal of parallel programming, ISSN 0885-7458, E-ISSN 1573-7640, Vol. 35, p. 477-491Article in journal (Refereed)
  • 10.
    Nordén, Markus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Löf, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Rantakokko, Jarmo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Geographical locality and dynamic data migration for OpenMP implementations of adaptive PDE solvers2006Report (Other academic)
    Abstract [en]

    On cc-NUMA multi-processors, the non-uniformity of main memory latencies motivates the need for co-location of threads and data. We call this special form of data locality, geographical locality. In this article, we study the performance of a parallel PDE solver with adaptive mesh refinement. The solver is parallelized using OpenMP and the adaptive mesh refinement makes dynamic load balancing necessary. Due to the dynamically changing memory access pattern caused by the runtime adaption, it is a challenging task to achieve a high degree of geographical locality.

    The main conclusions of the study are: (1) that geographical locality is very important for the performance of the solver, (2) that the performance can be improved significantly using dynamic page migration of misplaced data, (3) that a migrate-on-next-touch directive works well whereas the first-touch strategy is less advantageous for programs exhibiting a dynamically changing memory access patterns, and (4) that the overhead for such migration is low compared to the total execution time.

    Download full text (pdf)
    fulltext
  • 11.
    Thuné, Michael
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Åhlander, Krister
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Ljungberg, Malin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Nordén, Markus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Otto, Kurt
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Rantakokko, Jarmo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Object-oriented modeling of parallel PDE solvers2001In: The Architecture of Scientific Software, Norwell, MA: Kluwer Academic Publishers , 2001, p. 159-174Conference paper (Refereed)
1 - 11 of 11
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf