<i>An Introduction to High-Performance Scientific Computing</i>

Fosdick, Lloyd D.; Jessup, Elizabeth R.; Schauble, Carolyn J.C; Domik, Gitta

doi:10.1063/1.881590

Cited by 23 publications

(17 citation statements)

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“…Although, there exist dependencies between T5, T6-1 and T6-2 (see Figure 1), we can parallelize them using the pipeline model [5]. In fact, when a new candidate k-itemset from task T5, is generated, we can anticipate and count his frequency (by task T6-1).…”

Section: Hybrid Approach Using Pipeline Modelmentioning

confidence: 99%

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A Pipeline Model to Discover Frequent Itemsets in an Hierarchical Systems

Arour¹

2013

IJGDC

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Section: Hybrid Approach Using Pipeline Modelmentioning

confidence: 99%

“…-Although there exist dependencies between the the tasks T61 et T62, we could adopt the pipelining model to parallelize them [5]. …”

Section: Dependency Graphmentioning

confidence: 99%

A Pipeline Model to Discover Frequent Itemsets in an Hierarchical Systems

Arour¹

2013

IJGDC

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“…Our series of single-topic tutorials and short reference guides eventually became a textbook [3]. A laboratory manual and manuals for the machines and computational tools are still available on the Web [4].…”

Section: The Rise Of Hpscmentioning

confidence: 99%

Creating a Sustainable High-Performance Scientific Computing Course

Jessup

Tufo

2004

Computational Science - ICCS 2004

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Abstract. We describe our experiences with computational science and engineering (CS&E) education at the University of Colorado at Boulder in order to illustrate the difficulties associated with such a course of study. Our CS&E offerings began with a course on high-performance scientific computing (HPSC) developed under a CISE Educational Infrastructure grant from the National Science Foundation. The course was taught from 1991 through 1998 when various practical concerns ended its run. The course was revived in 2003 to meet the demands of several emerging programs in computational science. In this paper, we outline the rise, fall, and restoration of the HPSC course. We identify the technological developments that presently make such a course less labor intensive and more sustainable.

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“…Currently our model incorporates the visco-elastic brain tissue properties, initially set homogeneously within the brain, and the effect of gravity. In addition, our current research includes cerebrospinal fluid modeling motivated by interaction between fluid particles given by Lennard-Jones model (see [11], Chapter 15). Since the grey and white matter do not have the same visco-elastic properties the geometry of the sulci affects the brain deformation.…”

Section: D R E S U L T Smentioning

confidence: 99%

Brain shift modeling for use in neurosurgery

Škrinjar

Spencer

Duncan

1998

Medical Image Computing and Computer-Assisted Intervention — MICCAI’98

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Abstract. Surgical navigation systems are used intraoperatively to help the surgeon to ascertain her or his position and to guide tools within the patient frame with respect to registered structures of interest in the preoperative images. However, these systems are subject to inaccuracy caused by intraoperative brain movement (brain shift) since they assume that the intracranial structures are rigid. Experiments show brain shifts of up to several millimeters, making it the cause of the dominant error in those systems. We propose a method for reducing this error based on a dynamic brain model. The initial model state is obtained from preoperative data. The brain tissue is modeled as a homogeneous linear visco-elastic material, although the model allows for setting the tissue properties locally. Gravity draws the brain downwards which in turn interacts with the skull and other surrounding structures. The simulation results are presented both for a 2D model (the mid-sagittal slice) and a 3D model. The results show the time evolution of the brain deformation. The complete 3D validation of the simulated brain deformation is a rather complicated task and is currently in progress within our laboratory, but a procedure is proposed for updating the model in time by one or more of several intraoperative measurements. IntroductionThe use of surgical navigation systems is a standard way to assist the neurosurgeon in navigating within the intraoperative environment, allowing her or him to "see" through the body and relate the position of the surgical instruments to the features in preoperative images. It is important that these systems be as precise as possible. Ideally, they should provide a 3D display of the neuroanatomical structures of interest and include visualization of surgical instruments within the same frame. Furthermore, if 3D acquisition systems were fast enough, brain shift would not cause an error. Thus, the ideal goal would be to sense 3D intraoperative volumetric information in real time. Since the brain deforms rather slowly even a slower acquisition rate would be acceptable. However, hardware for obtaining true 3D data is limited probably to MR or CT and is typically bulky and expensive, restricting surgical access and therefore is not currently widely used in the OR 1.1 There exists a system using intraoperative MRI (see [5] and [6]) but it does not provide a complete 3D dataset of the head at a fast enough rate, but rather single slices at lower fields.

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An Introduction to High-Performance Scientific Computing

Cited by 23 publications

References 0 publications

A Pipeline Model to Discover Frequent Itemsets in an Hierarchical Systems

A Pipeline Model to Discover Frequent Itemsets in an Hierarchical Systems

Creating a Sustainable High-Performance Scientific Computing Course

Brain shift modeling for use in neurosurgery

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