Guide High-Performance Computing : Paradigm and Infrastructure

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Figure 1. The Australian high performance computing HPC environment including peak national facilities, specialized national facilities, and local HPC facilities. MASSIVE also manages a major nationally funded software infrastructure collaboration to make scientific tools, and in-particular neuroinformatics tools, available freely and cloud-ready. A Collaboration Agreement underpins the governance arrangements and includes a Steering Committee with an independent chair and members who are representatives of the partner organizations.

The facility provides an extensive program of user support and training on all aspects of high performance computing, and has an active outreach program to ensure that the MASSIVE stakeholders, Australian and international researchers, government and the broader community are aware of its benefits and achievements. Advanced imaging instruments, including CT and MRI scanners and electron and optical microscopes, are capable of producing data at an incredible rate. This introduces obvious challenges for researchers to capture, process, analyze, and visualize data in a timely and effective manner.

This configuration results in researchers moving their data only once, automatically during data capture, with subsequent processing, analysis, and visualization performed centrally on MASSIVE. Scientific applications of HPC, cloud and grid computing have been thoroughly documented and computing is considered an essential scientific tool Foster and Kesselman, A number of specialized undertakings for bioinformatics, and more specifically neuroinformatics, have been very successful and deserve particular comment. A number of projects provide dedicated HPC access and support to neuroimaging researchers.

These include the NeuGrid Redolfi Redolfi et al. All three projects provide web-based mechanisms for data management and processing and analysis on HPC systems, and specialized support for neuroimaging. In addition there are a number of online and desktop workflow environments that are being applied to general science and specific bioinformatics and neuroinformatics purposes.

These include Galaxy Giardine et al. These projects all provide mechanisms to interface with high performance computing resources. Nipype Gorgolewski et al. PSOM Bellec et al. The project commenced by undertaking to simulate a cellular-level model of a 2-week-old rat somatosensory neocortex based on captured microscopy data, specifically targeting the IBM Blue Gene HPC platform. MASSIVE shares many of the fundamental goals of these projects—to provide neuroscience researchers with access to high performance computing capabilities and data management.

However, our project differs in a number of ways:. MASSIVE consists of two interconnected computers, M1, and M2 respectively, that operate at over 5 and 30 teraflops 1 respectively, using traditional CPU processing, and accelerated to over 50 and teraflops 1 , respectively, using co-processors. M1 and the first stage of M2 were made available to Australian researchers in May The computers are connected using a dedicated connection for fast file transfer and common management. A summary of the technical specifications of the two systems and the hardware configuration of the two computers, including the GPU coprocessors and the parallel file systems, are given in Table 1.

Table 1. This has been critical to performing fast processing of data in a near real-time fashion as discussed in Section Instrument Integration Program. This capability has proved essential to support both the fast capture of data from instruments, and file system intensive image processing workloads. Section Instrument Integration Program discusses the importance of the file system to support large-scale and real-time CT reconstruction image processing applications. MASSIVE has a dedicated program for the integration of imaging instruments with high performance computing capability Figure 2 , Table 2 that gives scientists the ability to use complex and computationally demanding data processing workflows within minutes of acquiring image datasets.

Figure 2. Table 2. The instrument integration program allows scientists to visualize and analyse collected data as an experiment progresses or shortly after it completes, thereby integrating processing, analysis and visualization into the experiment itself. In particular, groups that are imaging live anesthetized animals must be able to establish whether a previous scan has successfully produced the desired data before proceeding with the next step of the experiment.

These experiments are typically time-critical as there is limited instrument availability once an experiment has commenced. In many cases the images captured by detectors at the Imaging Beamline are very large and necessitate the rapid movement of TB data sets for processing. These constraints dictate that significant computing power is required on demand and that the computer is tightly coupled to the instruments and readily available to the researchers.

Neuroimaging studies, especially multi-modal, longitudinal studies of large cohorts of subjects, generate large collections of data that need to be stored, archived, and accessed. MRI based studies can easily accumulate terabytes of data annually and require integration of HPC and informatics platforms with the imaging instrumentation.

Integrated systems that combine data, meta-data, and workflows are crucial for achieving the opportunities presented by advances in imaging facilities. Monash University hosts a multi-modality research imaging data management system that manages imaging data obtained from five biomedical imaging scanners operated at Monash Biomedical Imaging MBI Figure 3.

Research users can securely browse and download stored images and data, and upload processed data via subject-oriented informatics frameworks Egan et al. Figure 3. Schematic of the neuroscience image data flow from Monash Biomedical Imaging and the computational processing performed on M2.

DaRIS is designed to provide a tightly integrated path from instrument to repository to compute platform. With this framework, the DaRIS system at MBI manages the archiving, processing, and secure distribution of imaging data with the ability to handle large datasets acquired from biomedical imaging scanners and other data sources. This ensures long-term stability, usability, integrity, integration, and inter-operability of imaging data. Imaging data are annotated with meta-data according to a subject-centric data model and scientific users can find, download, and process data easily.

In this way, large subject-cohort projects can robustly process and re-process data with attendant enhanced data provenance. Current DaRIS enhancements are focusing on additional efficient data inter-operability capabilities so that researchers can access their managed data when and where they need it. The MASSIVE computers have been integrated with a number of beamlines at the Australian Synchrotron, and provide a range of data processing services to visiting researchers.

These include: near real-time image reconstruction at the IMBL, near-real time automated structural determination at the Macromolecular Crystallography beamline, microspectroscopy at the Infrared beamline, data analysis at the Small and Wide Angle Scattering beamlines, and image analysis at the X-ray Fluorescence Microprobe Beamline. These techniques are being applied to a range of biomedical sciences and neuroimaging applications. The IMBL has a capability of near real-time high-resolution CT imaging of a range of samples, including high-resolution phase-contrast x-ray imaging of biomedical samples, animal models used in neuroscience experiments, and engineering materials.

The beamline is meters long, with a satellite building that includes a medical suite for clinical research as well as extensive support facilities for biomedical and clinical research programs. Two detectors based on pco-edge cameras are available for use. Typical data acquisition times are dependent upon the chosen x-ray energy and detector resolution and vary approximately between 10 and 60 min for a complete CT scan.

Figure 4. In order to control the data collection and optimize the experimental conditions at IMBL, scientists must be able to visualize collected data in near real-time as the experiment is in progress. In particular, groups that are imaging live anesthetized animals often need to establish whether a previous scan has successfully produced the desired data before proceeding with the next step of an experiment. The experiments are typically time-critical as the window of the experiment once begun is short. The image datasets captured by detectors at the IMBL require the manipulation of data sets in the terabyte range.

These experimental constraints dictate that significant computing power is tightly coupled to the experimental detectors and available on-demand. CT data sets collected at IMBL are typically tens of GB per sample consisting of typically — projection images that can be acquired from a single sample in less than 1 min. The CT reconstruction service has been in production since November In particular, there is extensive functionality for X-ray CT image processing, including multiple methods for CT reconstruction and X-ray phase retrieval and simulation of phase-contrast imaging.

Additionally, a large number of operations such as FFT, filtering, algebraic, geometric, and pixel value operations are provided. The total reconstruction time and IO time as a proportion of the runtime for a set of CT reconstructions is shown in Figure 5 as a function of the number of CPU cores. The results demonstrate that IO represents a significant proportion of the overall running time—particularly beyond 36 CPU-cores.

We are currently investigating the refinement of the HPC based CT processing workflow to reduce the high proportion of IO time which is currently the major performance bottleneck. Figure 5. The total reconstruction time for CT reconstruction of an 3 dataset top and IO time as a proportion of runtime bottom on M1 as a function of the number of CPU cores. MASSIVE provides users with highly accessible high-performance scientific desktop—an interactive environment for analysis and visualization of multi-modal and multi-scale data Figure 6.

This environment provides researchers with access to a range of existing tools and software, including commercial and open-source neuroinformatics applications.

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Common neuroimaging applications such as FSL Smith et al. The continual growth in data and study sizes increasingly necessitates the analysis and rendering of data at the location where the data is stored. Furthermore, performing analysis and visualization on a central facility greatly increases the efficiency and flexibility for researchers to access high performance hardware, including fast file systems and GPUs.

Together with the MASSIVE Instrument Integration program, the desktop provides a fully integrated environment that allows researchers to view and analyze images shortly after the imaging data has been acquired. Figure 6. The launcher is provided for all three major desktop platforms.

It is configurable to other facilities and is being applied at other HPC facilities in Australia. It is available open source Section Software and System Documentation. The NeCTAR CVL project is an open source project aimed at porting key scientific imaging applications to the cloud with a particular focus on neuroinformatics tools Goscinski, The Neuroimaging Workbench has integrated workflow and database systems to allow researchers using instruments managed by the Australian National Imaging Facility NIF to process and manage large neuroimaging datasets.

The Australian NIF is a national network of universities and biomedical research institutes that provides key biomedical imaging instruments and capabilities for the Australian research community. Neuroinformatics tools in the cloud have great potential to accelerate research outcomes. The Neuroimaging Workbench includes a project for registration of multi-modal data brain data for the Australian Mouse Brain Mapping Consortium Richards et al.

Ultra-high resolution 15 um MRI and micro-CT images from excised tissue, can be registered with 3D reconstructions of histological stained microscopy sections. The registered datasets enable the MRI and CT images to be correlated at both the microscopic cellular and macroscopic whole organ scales.

A mouse brain atlas that combines ultra-high resolution MRI and histological images has wide ranging application in neuroscience. However, image registration of 3D microscopy and MRI datasets requires immense computational power as well as a range of specialized software tools and workflows, the developed workflow is applicable to all small animal atlas building efforts.

A major objective of the CVL Neuroimaging Workbench is to increase the efficiency for the neuroimaging community to undertake complex image processing and analyses for large and longitudinal scale studies. The integration of key imaging instruments across multiple nodes of NIF is allowing neuroimaging researchers to efficiently stage data to the cloud for processing on HPC facilities. The workbench provides researchers with simple and free access to a high performance desktop environment, that contains a fully configured set of neuroimaging tools for analysis and visualization, that may obviate the need for high-end desktop workstations that are currently replicated across many neuroimaging laboratories.

In addition, system documentation is available on request. Software developed under the Characterization Virtual Laboratory to support remote desktops and the neuroimaging workbench is available open source as they enter beta release www. The IMAGE-HD study is investigating the relationships between brain structure, microstructure and brain function with clinical, cognitive and motor deficits in both pre-manifest and symptomatic individuals with Huntington's disease.

Structural, functional, diffusion tensor, and susceptibility weighted MRI images have been acquired at three time points in over volunteers at study entry, and after 18 and 30 months. This data is managed in the DaRIS environment. Multi-modal imaging was used to identify sensitive biomarkers of disease progression for recommendation in future clinical trials. Longitudinal diffusion tensor imaging datasets have been analyzed using deterministic trackvis. The desktop is used to run semi-automated analysis pipelines for tracking longitudinal changes in structural connectivity, diffusivity in white matter, and functional connectivity in HD.

The desktop is also being to used to develop combined analyses of fMRI and DTI datasets in order to understand the relationships between brain functional and microstructural deficits in Huntington's disease. Diffusion guided QSM dQSM Ng, is a new technique that uses diffusion MRI data to improve the modeling of magnetic susceptibility at each position in the image, but it is a computationally challenging problem, requiring the inversion of a multi-terabyte matrix.

Diffusion guided QSM treats the magnetic susceptibility effect of each image voxel as isotropic Liu et al. The computation of the matrix formulation of the problem using the Landweber iteration LI method is prohibitively expensive on central processing unit CPU cores. Acceleration of the algorithm by utilizing graphics processing unit GPU cores is necessary to achieve image computation times practical for research use today, and for clinical application in the near future. The dQSM problem is suited to the GPU for the reason that the elements of the matrix in the Landweber iteration formulation can be computed on-demand; without this ability the problem would be intractable on GPUs.

Several attributes of the Landweber iteration method applied to the dQSM problem make it particularly suitable to the GPU architecture. Computing the solution requires iteratively multiplying very large matrices, which are computed on-the-fly from smaller input buffers, with vectors of voxel input data and adding the result to the previous values. This decomposition was applied to the GPU implementation to split separate sections of the problem over a number of GPUs in an additional layer of parallelism.

The fast interconnect between nodes enabled excellent scaling on the multiple GPU code with minimal communication overhead even when computed on up to 32 GPUs over 16 nodes. Current work involves a more intelligent load balancing of the work across multiple GPUs and potentially separating the problem into white-matter voxels which require the LI technique and therefore the huge level of compute power the GPU provides , and other voxels which can be computed using a fast Fourier transform based technique.

The major disadvantage is the very long computation time, which makes the method challenging for routine research and clinical applications. Algorithmic improvements and the growth in compute capability of GPUs together with the further speed-up of the GPU implementation being undertaken, is expected to enable clinically-relevant post-processing times less than 30 min.

Using multi-component models of tissue structures to estimate susceptibility effects will provide more accurate results with further improvements in implementation of the dQSM algorithm. The mouse is a vital model to elucidate the pathogenesis of human neurological diseases at a cellular and molecular level. The importance of the murine model in neuroscience research is demonstrated by the multitude and diversity of projects including the Allen Brain Atlas brain-map.

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Many research groups use non-invasive MRI to structurally map the murine brain in control and disease model cohorts. Until recently, the construction of mouse brain atlases has been relatively restricted due to the variety of sample preparation protocols and image sequences used, and the limited number of segmented brain regions. The AMBMC atlas has initially concentrated on five primary brain regions, the hippocampus, cortex, cerebellum, thalamus, and basal ganglia and has recently published a segmentation guide and probabilistic atlas for over structures.

These components have been integrated and made available through the Neuroimaging Workbench Janke, This includes technological trends, capabilities such as visualization, and major international initiatives. Because we commonly provide access to compute in a near-realtime or interactive manner, we must keep a proportion of the systems available and waiting for instrument processing or desktop sessions.

We are experimenting with strategies such as dynamic provisioning of nodes and short running jobs to fill idle time. Interactive desktop sessions on our facility run on a dedicated node. Thus, users have access to two CPU processors running between 8 and 12 cores, and up to GB of memory.

We do not allow multiple users onto a single desktop node, because a user can inadvertently affect other users. For example, by launching a multi-core application. However, a significant proportion of desktop users do not require access to the full technical capabilities. For example, a user that is using an image viewer to examine a large dataset might only require one CPU-core. The result is wasted computing resources. Our long-term plan to solve this problem is to host desktop sessions in virtual machines that will be provisioned at specific sizes and capabilities.

Using virtual machines allows us to completely isolate multiple users of a single desktop and ensure a good user experience. In our early experience with provisioning on the cloud Section Neuroinformatics in the Cloud the overhead imposed by a virtual machine is acceptable, but fast access to file systems needs to be carefully considered. Our most significant challenge is not technical but relates to user support. In a traditional HPC environment users will be accustomed to submitting jobs to a queue and checking back for their results.

In an interactive environment, small changes to performance and accessibility have a strong effect on user experience. Moreover, users require fast response to problems—particularly considering issues with the computing system can have a major effect a physical experiment. Our solution to this problem has been to ensure that have adequate expert staff who are able to quickly triage and prioritize problems. A major trend in HPC has been the application of GPU technology, developed primarily to support the gaming market, to enable fast parallel processing.

This would let us create a low cost manufacturing technology able to build any product including fast computing machines with atomic precision. Here we focus on extending the applicability of Twister to more kinds of scientific problems. Out of curiosity I wanted to know If Grid Computing is distributed computing extended over internet? Introduction to the Personal Computer Objectives Upon completion of this chapter, you should be able to answer the following questions: What are IT industry certifications?

What is a computer system?

Google Cloud Platform Diagram Example: High Performance Computing

How can I identify the names, purposes, and characteristics of cases and power supplies? What are the names, purposes, and characteris- Introduction to the Personal Computer Objectives Upon completion of this chapter, you should be able to answer the following questions: What are IT industry certifications? What are the names, purposes, and characteris- You will be able to better understand economic value creation and value appropriation, and learn the tools to analyze both competition and cooperation from a corporate level perspective, e.

Parallel processing is also associated with data locality and data communication. The computers in a distributed system are independent and do not physically share memory or processors. Implement parallel fast Fourier transform in Regent. CS Parallel and Distributed Processing 3 cr. Network, parallel and distributed computing. Driving Forces and Enabling Factors Desire and prospect for greater performance Users have even bigger problems and designers have even more gates 6 parallel and distributed computing lecture notes.

While tomographic and post-processing techniques become increasingly sophisticated, traditional and emerging modalities play more and more critical roles in anatomical, functional, cellular, and molecular imaging. Numerous practical application and commercial products that exploit this technology also exist.

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Our goal in this section is to decide which data management applications are best suited for deployment on top of cloud computing infrastructure. Loading Unsubscribe from Parallel Programming Course? Cancel Unsubscribe. Why Parallel Architecture? Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. Our new CrystalGraphics Chart and Diagram Slides for PowerPoint is a collection of over impressively designed data-driven chart and editable diagram s guaranteed to impress any audience.

In this paper, we explore the concept of cloud architecture and Parallel Imaging in MRI: Technology, Applications, and Quality Control 6 Preamble Task Group of the American Association of Physic ists in Medicine was established to describe the basis of parallel imaging in magnetic resonance imaging MRI and its applications to the medical physics community.

An efficient implementation of parallel FFT has many applications such as dark matter, plasma, and incompressible fluid simulations to name just a few! For those who are unfamiliar with Parallel Programming in general, the material covered in EC Introduction To Parallel Computing would be helpful. So the object could be assembled quickly by trillions of nano supercomputers working in parallel.

The scope of the series includes, but is not limited to, titles in the areas of scientific computing, parallel and distributed computing, high performance computing, grid computing, cluster computing, heterogeneous computing, quantum computing, and their Big Data Computing and Clouds: Trends and Future Directions Marcos D.

Calheirosb, Silvia Bianchic, Marco A. If you use an online service to send email, edit documents, watch movies or TV, listen to music, play games or store pictures and other files, it is likely that cloud computing is making it all possible behind the scenes. Prospects, parallel computing has not entered into widespread.

Introduction to Parallel Computing. Kernel-based parallel programming, multidimensional kernel configuration [] Watch 4d. Sanjay P. Process-centric methods, strategies and inter-organizational aspects of decision making are essential in the design and development of new technologies. Its just a matter of time when all other compute capable devices will be included to be able to compute as OpenCL and other APIs mature.

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The programmer has to figure out how to break the problem into pieces, and has to figure out how the pieces relate to each other. The articles in UCM. Register renaming. Students and practitioners alike will appreciate the relevant, up-to-date information. Branch prediction. Ahuja, Ph. This is especially true for all programs which take a significant amount of time to execute. In general though, in order for a program to take advantage of Pthreads, it must be able to be organized into discrete, independent tasks which can execute concurrently.

Parallel Programming Platforms. The HPP Heterogeneous Parallel Programming paradigm advocates the use of every different type of compute capable hardware that a system has for massively parallel computing. Can be Parallel Computing The term parallel computation is generally applied to any data processing, in which several computer instructions can. Covering these topics is beyond the scope of this tutorial, however interested readers can obtain a quick overview in the Introduction to Parallel Computing tutorial. Lectures will be interactive, drawing on readings from a new text - Parallel Computer.