177 research outputs found

    Efficient Implementations of Molecular Dynamics Simulations for Lennard-Jones Systems

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    Efficient implementations of the classical molecular dynamics (MD) method for Lennard-Jones particle systems are considered. Not only general algorithms but also techniques that are efficient for some specific CPU architectures are also explained. A simple spatial-decomposition-based strategy is adopted for parallelization. By utilizing the developed code, benchmark simulations are performed on a HITACHI SR16000/J2 system consisting of IBM POWER6 processors which are 4.7 GHz at the National Institute for Fusion Science (NIFS) and an SGI Altix ICE 8400EX system consisting of Intel Xeon processors which are 2.93 GHz at the Institute for Solid State Physics (ISSP), the University of Tokyo. The parallelization efficiency of the largest run, consisting of 4.1 billion particles with 8192 MPI processes, is about 73% relative to that of the smallest run with 128 MPI processes at NIFS, and it is about 66% relative to that of the smallest run with 4 MPI processes at ISSP. The factors causing the parallel overhead are investigated. It is found that fluctuations of the execution time of each process degrade the parallel efficiency. These fluctuations may be due to the interference of the operating system, which is known as OS Jitter.Comment: 33 pages, 19 figures, add references and figures are revise

    Techniques for the Automation of the Heap Exploit Synthesis Pipeline

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    Analysis of a benchmark suite to evaluate mixed numeric and symbolic processing

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    The suite of programs that formed the benchmark for a proposed advanced computer is described and analyzed. The features of the processor and its operating system that are tested by the benchmark are discussed. The computer codes and the supporting data for the analysis are given as appendices

    MMP

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 129-135).Reliability and security are quickly becoming users' biggest concern due to the increasing reliance on computers in all areas of society. Hardware-enforced, fine-grained memory protection can increase the reliability and security of computer systems, but will be adopted only if the protection mechanism does not compromise performance, and if the hardware mechanism can be used easily by existing software. Mondriaan memory protection (MMP) provides fine-grained memory protection for a linear address space, while supporting an efficient hardware implementation. MMP's use of linear addressing makes it compatible with current software programming models and program binaries, and it is also backwards compatible with current operating systems and instruction sets. MMP can be implemented efficiently because it separates protection information from program data, allowing protection information to be compressed and cached efficiently. This organization is similar to paging hardware, where the translation information for a page of data bytes is compressed to a single translation value and cached in the TLB. MMP stores protection information in tables in protected system memory, just as paging hardware stores translation information in page tables. MMP is well suited to improve the robustness of modern software. Modern software development favors modules (or plugins) as a way to structure and provide extensibility for large systems, like operating systems, web servers and web clients. Protection between modules written in unsafe languages is currently provided only by programmer convention, reducing system stability.(cont.) Device drivers, which are implemented as loadable modules, are now the most frequent source of operating system crashes (e.g., 85% of Windows XP crashes in one study [SBL03]). MMP provides a mechanism to enforce module boundaries, increasing system robustness by isolating modules from each other and making all memory sharing explicit. We implement the MMP hardware in a simulator and modify a version of the Linux 2.4.19 operating system to use it. Linux loads its device drivers as kernel module extensions, and MMP enforces the module boundaries, only allowing the device drivers access to the memory they need to function. The memory isolation provided by MMP increases Linux's resistance to programmer error, and exposed two kernel bugs in common, heavily-tested drivers. Experiments with several benchmarks where MMP was used extensively indicate the space taken by the MMP data structures is less than 11% of the memory used by the kernel, and the kernel's runtime, according to a simple performance model, increases less than 12% (relative to an unmodified kernel).by Emmett Jethro Witchel.Ph.D

    Simurgh: a fully decentralized and secure NVMM user space file system

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    The availability of non-volatile main memory (NVMM) has started a new era for storage systems and NVMM specific file systems can support extremely high data and metadata rates, which are required by many HPC and data-intensive applications. Scaling metadata performance within NVMM file systems is nevertheless often restricted by the Linux kernel storage stack, while simply moving metadata management to the user space can compromise security or flexibility. This paper introduces Simurgh, a hardware-assisted user space file system with decentralized metadata management that allows secure metadata updates from within user space. Simurgh guarantees consistency, durability, and ordering of updates without sacrificing scalability. Security is enforced by only allowing NVMM access from protected user space functions, which can be implemented through two proposed instructions. Comparisons with other NVMM file systems show that Simurgh improves metadata performance up to 18x and application performance up to 89% compared to the second-fastest file system.This work has been supported by the European Comission’s BigStorage project H2020-MSCA-ITN2014-642963. It is also supported by the Big Data in Atmospheric Physics (BINARY) project, funded by the Carl Zeiss Foundation under Grant No.: P2018-02-003.Peer ReviewedPostprint (author's final draft
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