公开(公告)号：US20250061003A1

公开(公告)日：2025-02-20

申请号：US18375492

申请日：2023-09-30

Applicant: Intel Corporation

Inventor： Stephen H. GUNTHER ,  Praveen Kumar GUPTA ,  Mahesh Kumar P ,  Monica GUPTA ,  Russell FENGER ,  Benjamin GRANIELLO

IPC: G06F9/50 ,  G06F11/34

Abstract: Techniques for providing hardware provided guidance for efficiently scheduling workloads to an optimal compute module are described. In some examples, hardware includes a first plurality of physical processor cores of a first type to implement a plurality of logical processor cores of the first type; a second plurality of physical processor cores of a second type, wherein each core of the second type is to implement a plurality of logical processor cores of the second type; a power management unit to monitor telemetry data on the first plurality of processor cores and second plurality of processor cores and to update hardware feedback telemetry data; and thread runtime telemetry circuitry to provide a hint using the hardware feedback telemetry data to consolidate tasks on one of core types.

公开(公告)号：US20220187893A1

公开(公告)日：2022-06-16

申请号：US17442374

申请日：2020-07-14

Applicant: INTEL CORPORATION

Inventor： Premanand SAKARDA ,  Efraim ROTEM ,  Eliezer WEISSMANN ,  Hisham ABU SALAH ,  Hadas BEJA ,  Russell FENGER ,  Deepak GANAPATHY ,  James HERMERDING, II ,  Ido KARAVANY ,  Nivedha KRISHNAKUMAR ,  Sudheer NAIR ,  Gilad OLSWANG ,  Moran PERI ,  Avishai WAGNER ,  Zhongsheng WANG ,  Noha YASSIN

IPC: G06F1/324 ,  G06F1/3296

Abstract: Described are mechanisms and methods for tracking user behavior profile over large time intervals and extracting observations for a user usage profile. The mechanisms and methods use machine learning (ML) algorithms embedded into a dynamic platform and thermal framework (DPTF) (e.g., Dynamic Tuning Technology) and predict device workloads using hardware (HW) counters. These mechanisms and methods may accordingly increase performance and user responsiveness by dynamically changing an Energy Performance Preference (EPP) based on a longer time workload analysis and workload prediction.