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    • 71. 发明申请
    • Optimizing layout of an application on a massively parallel supercomputer
    • 在大型并行超级计算机上优化应用程序的布局
    • US20060101104A1
    • 2006-05-11
    • US10963101
    • 2004-10-12
    • Gyan BhanotAlan GaraPhilip HeidelbergerEoin LawlessJames SextonRobert Walkup
    • Gyan BhanotAlan GaraPhilip HeidelbergerEoin LawlessJames SextonRobert Walkup
    • G06F1/16
    • G06F9/5066
    • A general computer-implement method and apparatus to optimize problem layout on a massively parallel supercomputer is described. The method takes as input the communication matrix of an arbitrary problem in the form of an array whose entries C(i, j) are the amount to data communicated from domain i to domain j. Given C(i, j), first implement a heuristic map is implemented which attempts sequentially to map a domain and its communications neighbors either to the same supercomputer node or to near-neighbor nodes on the supercomputer torus while keeping the number of domains mapped to a supercomputer node constant (as much as possible). Next a Markov Chain of maps is generated from the initial map using Monte Carlo simulation with Free Energy (cost function) F=Σi,jC(i,j)H(i,j)—where H(i,j) is the smallest number of hops on the supercomputer torus between domain i and domain j. On the cases tested, found was that the method produces good mappings and has the potential to be used as a general layout optimization tool for parallel codes. At the moment, the serial code implemented to test the method is un-optimized so that computation time to find the optimum map can be several hours on a typical PC. For production implementation, good parallel code for our algorithm would be required which could itself be implemented on supercomputer.
    • 描述了在大型并行超级计算机上优化问题布局的通用计算机实现方法和装置。 该方法采用数组形式的任意问题的通信矩阵作为输入,其条目C(i,j)是从域i到域j传送的数据量。 给定C(i,j),首先实现启发式映射,其尝试顺序地将域及其通信邻居映射到超级计算机节点或超级计算机环面上的近邻节点,同时保持域的数量映射到 超级计算机节点常数(尽可能多)。 接下来,使用具有自由能量(成本函数)的蒙特卡罗模拟,从初始映射生成马尔可夫链映射,其中F =Σi,j C(i,j)H(i,j) H(i,j)是域i和域j之间的超级计算机环面上的最小跳数。 在测试的情况下,发现该方法产生良好的映射,并且有可能被用作并行代码的通用布局优化工具。 此时,实现测试方法的序列号未优化,以便在典型的PC上找到最佳映射的计算时间可以为几个小时。 对于生产实现,将需要我们的算法的良好的并行代码,这本身可以在超级计算机上实现。
    • 73. 发明授权
    • Compression store free-space management
    • 压缩店自由空间管理
    • US07024512B1
    • 2006-04-04
    • US09021333
    • 1998-02-10
    • Peter Anthony FranaszekPhilip Heidelberger
    • Peter Anthony FranaszekPhilip Heidelberger
    • G06F12/00G06F9/00
    • G06F12/023G06F12/08G06F2212/401Y10S707/99942
    • An improved method, system, and a computer program storage device (e.g., including software embodied on a magnetic, electrical, optical, or other storage device) for management of compressed main memory allocation and utilization which can avoid system abends or inefficient operation that would otherwise result. One feature reduces (and ultimately eliminates) all unessential processing as the amount of available storage decreases to a point low enough to threaten a system abend. In another example, the amount of current memory usage is determined as well as one or more of: an estimate of an amount of allocated but unused memory; a determination of the amount of memory required for outstanding I/O requests. The compressed memory is managed as a function of the current memory usage and one or more of the other measured or estimated quantities. The compressed memory can be managed by maintaining a set of dynamic thresholds; estimating the amount of storage that can easily be freed (used but available) and the amount of storage that is committed (allocated but unused). The estimate of committed storage can include: the current storage utilization; and an estimate of storage committed to new pages (based on the number of new pages granted), the times at which this was done, the estimated compression ratio, and estimates of residency times in the cache.
    • 改进的方法,系统和计算机程序存储设备(例如,包括在磁性,电气,光学或其他存储设备上实现的软件),用于管理压缩的主存储器分配和利用,其可以避免系统退出或低效的操作, 否则结果。 一个功能减少(并最终消除)所有不必要的处理,因为可用存储空间的数量减少到足以威胁系统退出的程度。 在另一示例中,确定当前存储器使用量,以及以下中的一个或多个:已分配但未使用的存储器的量的估计; 确定未完成的I / O请求所需的内存量。 根据当前内存使用情况和一个或多个其他测量或估计的数量来管理压缩存储器。 可以通过维护一组动态阈值来管理压缩存储器; 估计可以轻松释放(已用但可用)的存储量和已提交(已分配但未使用)的存储量。 承诺存储的估计可以包括:当前的存储利用率; 以及对新页面的存储量的估计(基于授予的新页面数量),完成的时间,估计的压缩率以及高速缓存中的驻留时间的估计。