Nvidia Corporation

Siddharth Sharma

Emmett Michael Kilgariff

Dale L Kirkland

Rouslan Lyubomirov Dimitrov

Sachin Satish Idgunji

Alan Menezes

Timothy J McDonald

Thomas Melvin Ogletree

Pranav Vaidya

Abhijat Ranade

Gregory Paul Smith

Jin Ouyang

Various embodiments include a system for generating performance monitoring data in a computing system. The system includes a unit level counter with a set of counters, where each counter increments during each clock cycle in which a corresponding electronic signal is at a first state, such as a high or low logic level state. Periodically, the unit level counter transmits the counter values to a corresponding counter collection unit. The counter collection unit includes a set of counters that aggregates the values of the counters in multiple unit level counters. Based on certain trigger conditions, the counter collection unit transmits records to a reduction channel. The reduction channel includes a set of counters that aggregates the values of the counters in multiple counter collection units. Each virtual machine executing on the system can access a different corresponding reduction channel, providing secure performance metric data for each virtual machine.

Generating power efficient secure multiclient performance monitoring data

Graphics processing unit (GPU) performance and power efficiency is improved using machine learning to tune operating parameters based on performance monitor values and application information. Performance monitor values are processed using machine learning techniques to generate model parameters, which are used by a control unit within the GPU to provide real-time updates to the operating parameters. In one embodiment, a neural network processes the performance monitor values to generate operating parameters in real-time.

Real-time hardware-assisted GPU tuning using machine learning

Growing community of inventors

Milpitas, CA, United States of America

Siddharth Sharma