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  • 1.
    Djurfeldt, Mikael
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lundqvist, Mikael
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Johansson, Christopher
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Rehn, Martin
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Ekeberg, Örjan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Lansner, Anders
    KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
    Brain-scale simulation of the neocortex on the IBM Blue Gene/L  supercomputer2008Inngår i: IBM Journal of Research and Development, ISSN 0018-8646, E-ISSN 2151-8556, Vol. 52, nr 1-2, s. 31-41Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Biologically detailed large-scale models of the brain can now be simulated thanks to increasingly powerful massively parallel supercomputers. We present an overview, for the general technical reader, of a neuronal network model of layers II/III of the neocortex built with biophysical model neurons. These simulations, carried out on an IBM Blue Gene/Le supercomputer, comprise up to 22 million neurons and 11 billion synapses, which makes them the largest simulations of this type ever performed. Such model sizes correspond to the cortex of a small mammal. The SPLIT library, used for these simulations, runs on single-processor as well as massively parallel machines. Performance measurements show good scaling behavior on the Blue Gene/L supercomputer up to 8,192 processors. Several key phenomena seen in the living brain appear as emergent phenomena in the simulations. We discuss the role of this kind of model in neuroscience and note that full-scale models may be necessary to preserve natural dynamics. We also discuss the need for software tools for the specification of models as well as for analysis and visualization of output data. Combining models that range from abstract connectionist type to biophysically detailed will help us unravel the basic principles underlying neocortical function.

  • 2. Ho, Ching-Tien
    et al.
    Johnsson, Lennart
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    Embedding Hyper–pyramids in Hypercubes1994Inngår i: IBM Journal of Research and Development, ISSN 0018-8646, E-ISSN 2151-8556, Vol. 38, nr 1, s. 31-45Artikkel i tidsskrift (Fagfellevurdert)
  • 3. Martorell, X
    et al.
    Smeds, Nils
    KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.
    Walkup, R
    Brunheroto, J R
    Almási, G
    Gunnels, J A
    DeRose, L
    Labarta, J
    Escalé, F
    Giménez, J
    Servat, H
    Moreira, J E
    Blue Gene/L performance tools2005Inngår i: IBM Journal of Research and Development, ISSN 0018-8646, E-ISSN 2151-8556, Vol. 49, nr 2-3, s. 407-424Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Good performance monitoring is the basis of modern performance aualysis tools for application optimization. We are providing a variety of such performance analysis tools for the new Blue Gene(®)/L supercomputer. Those tools can be divided into two categories: single-node performance tools and multinode performance tools. Front a single-node perspectire, we provide standard interfaces and libraries, such as PAPI and libHPM, that propide access to the hardware performance counters for applications running on the Blue Gene/L compute nodes. From a multinode perspective, we focus on tools that analyze Message Passing Interface (MPI) behavior. Those tools work by first collecting message-passing trace data when a program runs. The trace data is then used by, graphical interface tools that analyze the behavior of applications. Using the current prototype tools, we demonstrate their usefulness and applicability with case studies of application optimization.

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