公开(公告)号：US11861408B2

公开(公告)日：2024-01-02

申请号：US17351849

申请日：2021-06-18

Applicant: Google LLC

Inventor： Shrikant Kelkar ,  Lakshmi Sharma ,  Manoj Jayadevan ,  Gargi Adhav ,  Parveen Patel ,  Parthasarathy Ranganathan

IPC: G06F9/46 ,  G06F9/50 ,  G06F9/48

CPC classification number: G06F9/5044 ,  G06F9/4881 ,  G06F9/505 ,  G06F9/5061

Abstract: The present disclosure includes systems, methods, and computer-readable mediums for discovering capabilities of a hardware (HW) accelerator card. A processor may communicate a request for a listing of acceleration services to a HW accelerator card connected to the processor via the communication interface. The HW accelerator card may retrieve the listing from memory and provide a response to the processor that includes a listing of the HW acceleration services provided by the HW accelerator card.

公开(公告)号：US20230297580A1

公开(公告)日：2023-09-21

申请号：US17721873

申请日：2022-04-15

Applicant: Google LLC

Inventor： Sheng Li ,  Garrett Axel Andersen ,  Norman Paul Jouppi ,  Quoc V. Le ,  Liqun Cheng ,  Parthasarathy Ranganathan ,  Julian Paul Grady ,  Yang Li ,  Martin Wicke ,  Yifeng Lu ,  Yun Ni ,  Kun Wang

IPC: G06F16/2457 ,  G06F16/2455 ,  G06N3/063

CPC classification number: G06F16/2457 ,  G06F16/24554 ,  G06N3/063

Abstract: According to various implementations, generally disclosed herein is a hybrid and hierarchical neural architecture search (NAS) approach. The approach includes performing a search space partitioning scheme to divide the search space into sub-search spaces. The approach further includes performing a first type of NAS, such as a Multi-trial NAS, to cover a search across the sub-search spaces. The approach also includes performing a second type of NAS, such as a One-Shot NAS, to cover each sub-search space. The approach further includes automatically stopping the second type of NAS based on one or more early stopping criteria.

公开(公告)号：US20230153159A1

公开(公告)日：2023-05-18

申请号：US17525300

申请日：2021-11-12

Applicant: Google LLC

Inventor： Shrikant Kelkar ,  Gargi Adhav ,  Lakshmi Sharma ,  Manoj Jayadevan ,  Parveen Patel ,  Parthasarathy Ranganathan

IPC: G06F9/50 ,  G06F9/38

CPC classification number: G06F9/505 ,  G06F9/3877

Abstract: The present disclosure includes systems, methods, and computer-readable mediums for discovering capabilities of local and remote hardware (HW) accelerator cards. A local hardware (HW) accelerator card may provide, via a communication interface, a listing of acceleration services from the local HW accelerator card. The listing of acceleration services may include a first set of acceleration services provided by one or more accelerators of the local HW accelerator card and a second set of acceleration services provided by one or more accelerators of a remote HW accelerator card. A workload instruction defining a workload for processing by at least one of the acceleration services of the second set of acceleration services may be received from a processor of a computing device. The workload instruction may be forwarded to the remote HW accelerator card.

公开(公告)号：US20220229698A1

公开(公告)日：2022-07-21

申请号：US17150285

申请日：2021-01-15

Applicant: Google LLC

Inventor： David Lo ,  Liqun Cheng ,  Parthasarathy Ranganathan ,  Sundar Jayakumar Dev

IPC: G06F9/50 ,  G06N20/00

Abstract: The subject matter described herein provides systems and techniques to address the challenges of growing hardware and workload heterogeneity using a Warehouse-Scale Computer (WSC) design that improves the efficiency and utilization of WSCs. The WSC design may include an abstraction layer and an efficiency layer in the software stack of the WSC. The abstraction layer and the efficiency layer may be designed to improve job scheduling, simplify resource management, and drive hardware-software co-optimization using machine learning techniques and automation in order to customize the WSC for applications at scale. The abstraction layer may embrace platform/hardware and workload diversity through greater coordination between hardware and higher layers of the WSC software stack in the WSC design. The efficiency layer may employ machine learning techniques at scale to realize hardware/software co-optimizations as a part of the autonomous WSC design.

公开(公告)号：US10650001B2

公开(公告)日：2020-05-12

申请号：US15726130

申请日：2017-10-05

Applicant: Google LLC

Inventor： Milad Olia Hashemi ,  Parthasarathy Ranganathan ,  Harsh Satija

IPC: G06F17/30 ,  G06F16/2455 ,  G06N3/08 ,  G06N3/04 ,  G05B13/00 ,  G06F16/176 ,  G06F16/2453 ,  G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for disaggregating latent causes for computer system optimization. In one aspect, a method includes accessing a data stream for data values resulting from operations performed by a computer system; providing the data values as input to a data disaggregation machine learning model that generates descriptors of latent causes of the data values; providing the data values and the descriptors of the latent causes of the data values as inputs to a control system model that generates embedded representations of commands to modify the operations performed by the computer system; determining commands to modify the operations performed by the computer system based on the embedded representations of commands to modify the operations performed by the computer system; and providing the commands to the computer system.

公开(公告)号：US20190236010A1

公开(公告)日：2019-08-01

申请号：US16379303

申请日：2019-04-09

Applicant: Google LLC

Inventor： Richard Yoo ,  Liqun Cheng ,  Benjamin C. Serebrin ,  Parthasarathy Ranganathan ,  Rama Krishna Govindaraju

IPC: G06F12/0811 ,  G06F9/4401 ,  G06F1/3234 ,  G06F12/0897 ,  G06F12/0871

CPC classification number: G06F12/0811 ,  G06F1/3275 ,  G06F9/4401 ,  G06F12/0871 ,  G06F12/0897 ,  G06F2212/283

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for caching data not frequently accessed. One of the methods includes receiving a request for data from a component of a device, determining that the data satisfies an infrequency condition, in response to determining that the data satisfies the infrequency condition: determining a target cache level which defines a cache level within a cache level hierarchy of a particular cache at which to store infrequently accessed data, the target cache level being lower than a highest cache level in the cache level hierarchy, requesting and receiving the data from a memory that is not a cache of the device, and storing the data in a level of the particular cache that is at or below the target cache level in the cache level hierarchy, and providing the data to the component.

公开(公告)号：US10191672B2

公开(公告)日：2019-01-29

申请号：US14885786

申请日：2015-10-16

Applicant: Google LLC

Inventor： Rama Krishna Govindaraju ,  Liqun Cheng ,  Parthasarathy Ranganathan ,  Michael R. Marty ,  Andrew Gallatin

IPC: G06F3/06 ,  G06F12/1081 ,  G06F12/0888

Abstract: An example method includes during execution of a software application by a processor, receiving, by a copy processor separate from the processor, a request for an asynchronous data copy operation to copy data within a memory accessible by the copy processor, wherein the request is received from a copy manager accessible by the software application in a user space of an operating system managing execution of the software application; in response to the request, initiating, by the copy processor, the asynchronous data copy operation; continuing execution of the software application by the processor; determining, by the copy processor, that the asynchronous data copy operation has completed; and in response to determining that the asynchronous copy operation has completed, selectively notifying, by the copy processor, the software application that the asynchronous copy operation has completed.