Nvidia Corporation

Lei Yang

Emmett Michael Kilgariff

Eric Brian Lum

Rouslan Lyubomirov Dimitrov

Henry Packard Moreton

Jack H Choquette

Shirish C Gadre

Lacky V Shah

Walter E Donovan

Yury Y Uralsky

Rui Bastos

Omkar Paranjape

Gentaro Hirota

Gregory Scott Palmer

Rajballav Dash

Chris Amsinck

Matthijs De Smedt

Sriharsha Niverty

Milton Lei

The present technology augments the GPU compute model to provide system-provided data marshalling characteristics of graphics pipelining to increase efficiency and reduce overhead. A simple scheduling model based on scalar counters (e.g., semaphores) abstract the availability of hardware resources. Resource releases can be done programmatically, and a system scheduler only needs to track the states of such counters/semaphores to make work launch decisions. Semantics of the counters/semaphores are defined by an application, which can use the counters/semaphores to represent the availability of free space in a memory buffer, the amount of cache pressure induced by the data flow in the network, or the presence of work items to be processed.

Programming model for resource-constrained scheduling

A parallel processing unit (e.g., a GPU), in some examples, includes a hardware scheduler and hardware arbiter that launch graphics and compute work for simultaneous execution on a SIMD/SIMT processing unit. Each processing unit (e.g., a streaming multiprocessor) of the parallel processing unit operates in a graphics-greedy mode or a compute-greedy mode at respective times. The hardware arbiter, in response to a result of a comparison of at least one monitored performance or utilization metric to a user-configured threshold, can selectively cause the processing unit to run one or more compute work items from a compute queue when the processing unit is operating in the graphics-greedy mode, and cause the processing unit to run one or more graphics work items from a graphics queue when the processing unit is operating in the compute-greedy mode. Associated methods and systems are also described.

Simultaneous compute and graphics scheduling

Motion adaptive shading increases rendering performance for real-time animation in graphics systems while maintaining dynamic image quality. Each frame of an animation is statically displayed within a refresh interval, while a viewer's eyes move continuously relative to the image when actively tracking a moving object being displayed. As a result, a statically displayed frame is essentially smeared across the viewer's continuously moving retina over the lifetime of the frame, causing a perception of blur referred to as an eye-tracking motion blur effect. A region of an image depicting a moving object may be rendered at a lower shading rate because eye-tracking motion blur will substantially mask any blur introduced by reducing the shading rate. Reducing an average shading rate for rendering frames reduces computational effort per frame and may advantageously allow a rendering system to operate at a higher frame rate to provide a smoother, clearer visual experience.

Motion adaptive rendering using variable rate shading

One aspect of the current disclosure provides a method of upscaling an image. The method includes: rendering an image, wherein the rendering includes generating color samples of the image at a first resolution and depth samples of the image at a second resolution, which is higher than the first resolution; and upscaling the image to an upscaled image at a third resolution, which is higher than the first resolution, using the color samples and the depth samples.

HW-assisted upscaling and multi-sampling using a high resolution depth buffer

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Santa Clara, CA, United States of America

Lei Yang