US 11,030,012 B2Granted

Methods and apparatus for allocating a workload to an accelerator using machine learning

AssigneeIntel Corporation

InventorsDivya Vijayaraghavan, Denica Larsen, Kooi Chi Ooi, Lady Nataly Pinilla Pico, Min Suet Lim

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Abstract

Methods, apparatus, systems, and articles of manufacture for allocating a workload to an accelerator using machine learning are disclosed. An example apparatus includes a workload attribute determiner to identify a first attribute of a first workload and a second attribute of a second workload. An accelerator selection processor causes at least a portion of the first workload to be executed by at least two accelerators, accesses respective performance metrics corresponding to execution of the first workload by the at least two accelerators, and selects a first accelerator of the at least two accelerators based on the performance metrics. A neural network trainer trains a machine learning model based on an association between the first accelerator and the first attribute of the first workload. A neural network processor processes, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.

Claims17

Dependent claims

An apparatus for executing a workload, the apparatus comprising:

a workload attribute determiner to identify a first attribute of a first workload, the first attribute representing whether the first workload would cause an accelerator to use at least a threshold amount of memory, the workload attribute determiner to identify a second attribute of a second workload;
an accelerator selection processor to:
in response to the first attribute representing that the first workload would not cause the accelerator to use at least the threshold amount of memory, cause at least a portion of the first workload to be executed by a first accelerator and a second accelerator;
in response to the first attribute representing that the first workload would cause the accelerator to use at least the threshold amount of memory, cause at least the portion of the first workload to be executed by the first accelerator and cause at least the portion of the first workload to not be executed by the second accelerator;
and
access respective performance metrics corresponding to execution of the first workload by the first accelerator and the second accelerator;
a neural network trainer to train a machine learning model based on an association between the first attribute and the performance metrics;
and
a neural network processor to process, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.

6 more elements

At least one non-transitory machine-readable medium comprising instructions that, when executed, cause at least one processor to at least:

identify a first attribute of a first workload, the first attribute representing whether the first workload would cause an accelerator to use at least a threshold amount of memory;
in response to the first attribute representing that the first workload would not cause the accelerator to use at least the threshold amount of memory, cause at least a portion of the first workload to be executed by a first accelerator and a second accelerator;
in response to the first attribute representing that the first workload would cause the accelerator to use at least the threshold amount of memory, cause at least the portion of the first workload to be executed by the first accelerator and cause at least the portion of the first workload to not be executed by the second accelerator;
access respective performance metrics corresponding to execution of the first workload by the first accelerator and the second accelerator;
train a machine learning model based on an association between the first attribute and the performance metrics;
identify a second attribute of a second workload;
and
process, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.

5 more elements

An apparatus for allocation of workloads to an accelerator, the apparatus comprising:

means for identifying a first attribute of a first workload, the first attribute representing whether the first workload would cause an accelerator to use at least a threshold amount of memory;
means for causing, in response to the first attribute representing that the first workload would not cause the accelerator to use at least the threshold amount of memory, at least a portion of the first workload to be executed by a first accelerator and a second accelerator;
means for causing, in response to the first attribute representing that the first workload would cause the accelerator to use at least the threshold amount of memory, at least the portion of the first workload to be executed by the first accelerator and at least the portion of the first workload to not be executed by the second accelerator;
means for accessing respective performance metrics corresponding to execution of the first workload by the first accelerator and the second accelerator;
means for training a machine learning model based on an association between the first attribute and the performance metrics;
means for identifying a second attribute of a second workload;
and
means for processing the second attribute of the second workload to select one of the at least two accelerators to execute the second workload.

5 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/146,845Filed 28 Sep 2018
Published: US 2019/0050265 A114 Feb 2019
Granted: 8 Jun 2021
Term ends: Not estimatedThe source records don’t support an estimate.

Classification9 codes

G06F 9/5027
To service a request: the resource being a machine, e.g. CPUs, Servers, Terminals
G06F 9/5044
The resource being a machine, e.g. CPUs, Servers, Terminals: considering hardware capabilities
G06F 11/3024
Monitoring arrangements specially adapted to the computing system or computing system component being monitored: where the computing system…
G06F 18/214
Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation: generating training…

Family

US 2019/0050265 A1Pre-grant publication · published 14 Feb 2019
US 11,030,012 B2This patent

From the US filing history. Foreign family members aren’t listed.

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