公开(公告)号：US09507641B1

公开(公告)日：2016-11-29

申请号：US14709385

申请日：2015-05-11

Applicant: QUALCOMM INCORPORATED

Inventor： James Michael Artmeier ,  Sumit Sur ,  Robert Scott Dreyer ,  Michael Douglas Sharp ,  James Lyall Esliger

IPC: G06F9/46 ,  G06F9/50

CPC classification number: G06F9/5044 ,  G06F9/505 ,  G06F9/5066 ,  G06F9/5083 ,  Y02D10/22 ,  Y02D10/36

Abstract: Systems and methods for dynamic granularity control of parallelized work in a heterogeneous multi-processor portable computing device (PCD) are provided. During operation a first parallelized portion of an application executing on the PCD is identified. The first parallelized portion comprising a plurality of threads for parallel execution on the PCD. Performance information is obtained about a plurality of processors of the PCD, each of the plurality of processors corresponding to one of the plurality of threads. A number M of workload partition granularities for the plurality of threads is determined, and a total execution cost for each of the M workload partition granularities is determined. An optimal granularity comprising a one of the M workload partition granularities with a lowest total execution cost is determined, and the first parallelized portion is partitioned into a plurality of workloads having the optimal granularity.

Abstract translation: 提供了一种用于异构多​​处理器便携式计算设备（PCD）中并行工作的动态粒度控制的系统和方法。 在操作期间，识别在PCD上执行的应用程序的第一并行化部分。 第一并行部分包括用于在PCD上并行执行的多个线程。 获得关于PCD的多个处理器的性能信息，所述多个处理器中的每一个对应于所述多个线程之一。 确定多个线程的M个工作负载分区粒度，并且确定每个M个工作负载分区粒度的总执行成本。 确定包括具有最低总执行成本的M个工作负载分区粒度之一的最佳粒度，并且将第一并行化部分划分为具有最佳粒度的多个工作负载。

公开(公告)号：US09940109B2

公开(公告)日：2018-04-10

申请号：US14803110

申请日：2015-07-20

Applicant: QUALCOMM INCORPORATED

Inventor： James Lyall Esliger ,  Wilson Kwan ,  Mark Bapst

IPC: G06F9/45 ,  G06F1/32 ,  G06F11/34 ,  G06F9/50 ,  G06F9/44

CPC classification number: G06F8/41 ,  G06F1/324 ,  G06F1/329 ,  G06F1/3296 ,  G06F8/443 ,  G06F8/71 ,  G06F9/50 ,  G06F11/3409 ,  G06F11/3442 ,  Y02D10/22

Abstract: Various embodiments of methods and systems for proactive resource allocation and configuration are disclosed. An exemplary method first compiles and links a profile instrumented application with a compiler comprising a profile guided optimization feature that inserts calls to a profiler runtime. The profile instrumented application is executed on a target device using one or more workload datasets representative of probable workloads. During execution, based on recognition of the inserted calls, an instrumentation-based profile dataset is generated in association with each of the one or more workload datasets. Next, the profile instrumented application is recompiled and relinked based on the instrumentation-based profile datasets to create a set of profile guided optimizations to the source code, thereby resulting in an optimized application. The optimized application may be executed and monitored to generate a revised profile dataset useful for providing instructions to the target device for optimal workload allocation and resource configuration.