公开(公告)号：US11960936B2

公开(公告)日：2024-04-16

申请号：US17150285

申请日：2021-01-15

Applicant: Google LLC

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

IPC: G06F9/46 ,  G06F9/50 ,  G06N20/00

CPC classification number: G06F9/5027 ,  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：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.