REDUCING BURN-IN FOR MONTE-CARLO SIMULATIONS VIA MACHINE LEARNING

    公开(公告)号:US20220147668A1

    公开(公告)日:2022-05-12

    申请号:US17094690

    申请日:2020-11-10

    Abstract: Techniques are disclosed for compressing data. The techniques include identifying, in data to be compressed, a first set of values, wherein the first set of values include a first number of two or more consecutive identical non-zero values; including, in compressed data, a first control value indicating the first number of non-zero values and a first data item corresponding to the consecutive identical non-zero values; identifying, in the data to be compressed, a second value having an exponent value included in a defined set of exponent values; including, in the compressed data, a second control value indicating the exponent value and a second data item corresponding to a portion of the second value other than the exponent value; and including, in the compressed data, a third control value indicating a third set of one or more consecutive zero values in the data to be compressed.

    AUTOMATED USE OF COMPUTATIONAL MOTIFS VIA DEEP LEARNING DETECTION

    公开(公告)号:US20230205517A1

    公开(公告)日:2023-06-29

    申请号:US17562921

    申请日:2021-12-27

    CPC classification number: G06F8/71 G06F8/4434 G06F8/4432 G06F9/45516

    Abstract: A system and method are described for efficiently utilizing optimized implementations of computational patterns in an application. In various implementations, a computing system includes at least one or more processors, and these one or more processors and other hardware resources of the computing system process a variety of applications. Sampled, dynamic values of hardware performance counters are sent to a trained data model. The data model provides characterization of the computational patterns being used and the types of workloads being processed. The data model also indicates whether the identified computational patterns already use an optimized version. Later, a selected processor determines a given unoptimized computational pattern is no longer running and replaces this computational pattern with an optimized version. Although the application is still running, the processor performs a static replacement. On a next iteration of the computational pattern, the optimized version is run.

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