Intel Corporation

Karol A Szerszen

Prasoonkumar Surti

Abhishek R Appu

Vasanth Ranganathan

Altug Koker

Karthik Vaidyanathan

Saikat Mandal

Joydeep Ray

Vamsee Vardhan Chivukula

Saurabh Sharma

Eric G Liskay

Luis F Cruz Camacho

Akshay R Chada

Gabor Liktor

Sven Woop

Robert L Farrell

Aleksander Olek Neyman

Art Hunter

Ronald W Silvas

Sreenivas Kothandaraman

Shashank Lakshminarayana

Sudhakar Kamma

Abhishek Rhisheekesan

Priyanka Ladda

Rajasekhar Pantangi

Subramaniam M Maiyuran

Aditya Navale

Michael J Norris

Jorge F Garcia Pabon

Alexey Supikov

Vidhya Krishnan

Hiroshi Akiba

Won-Jong Lee

Timothy Rowley

Andrew S Downsworth

Bryon T Rogers

Mohamed Farook

Takahiro Murata

A system includes a compression engine that stores the compression format information embedded in the compressed data. The compression format information can be included in a header that includes compression control surface (CCS) information. The system includes a shared memory to store compressed data for multiple hardware pipelines, where blocks of the compressed data have a common memory footprint and the compression header. The compression engine can compress data to store in the shared memory including generation of the header. The compression engine can decompress data read from the shared memory, including identification of the compression format from the header.

Unified stateless compression system for universally consumable compression

Embodiments are generally directed to compression for compression for sparse data structures utilizing mode search approximation. An embodiment of an apparatus includes one or more processors including a graphics processor to process data; and a memory for storage of data, including compressed data. The one or more processors are to provide for compression of a data structure, including identification of a mode in the data structure, the data structure including a plurality of values and the mode being a most repeated value in a data structure, wherein identification of the mode includes application of a mode approximation operation, and encoding of an output vector to include the identified mode, a significance map to indicate locations at which the mode is present in the data structure, and remaining uncompressed data from the data structure.

Compression for sparse data structures utilizing mode search approximation

Examples described herein relate to a manner of determining a number of bits to encode compression data. Some examples include: compressing pixel data of a region of pixels in a frame; determining a number of bits associated with at least two partitions; utilizing the determined number of bits to encode residual values generated from the compressing the pixel data; and storing the encoded residual values. In some examples, the at least two partitions comprise a first partition and a second partition. Some examples include: encoding residuals in the first partition using a number of bits associated with the first partition and encoding residuals in the second partition using a number of bits associated with the second partition. Some examples include: determining a distribution of bins of residuals, wherein each different bin represents a number of bits used to encode a residual value and determining a midpoint of a total number of residuals as a bin that stores a residual that is approximately 50 percentile of the total number of residuals of the distribution.

Memory allocation technologies for data compression and de-compression

A graphics processing apparatus includes graphics processors connected by a network connection, where the graphics processors pass compressed data. A first graphics processor stores data blocks as compressed data in a memory. The compressed data has data blocks of variable size, where a size of a block of compressed data depends on a compression ratio of the block of compressed data. A second graphics processor also stores data blocks as compressed data. The first graphics processor concatenates a variable number of blocks of compressed data into a packet of fixed size to send to the second graphics processor. The packet has a variable number of blocks of compressed data depending on the compression ratios of the multiple blocks of compressed data.

Scale up and out compression

Methods, systems and apparatuses may provide for technology that identifies first graphics data that is associated with spatially proximate positions. The technology identifies second graphics data that is associated with spatially proximate positions, and interleaves the first and the second graphics data across a plurality of storage tiles.

Compression and interleaving of spatially proximate data

Generation and storage of compressed z-planes in graphics processing is described. An example of a processor includes a rasterizer to generate a fragment of pixel data including blocks of pixel data; a depth pipeline to receive the fragment, the pipeline including a first and second depth test hardware, the first depth test hardware to perform a coarse depth test including determining minimum and maximum depths for each block; and a depth buffer, wherein the processor is to determine whether the fragment meets requirements that the fragment fully covers a tile of pixel data and passes a first depth test, and that each of the minimum and maximum depths of the fragment has a same sign and exponent, and, upon determining that the fragment meets the requirements, to generate a compressed depth plane utilizing the first depth test and update the depth buffer with the compressed depth plane.

Generation and storage of compressed Z-planes in graphics processing

Apparatus and method for grouping rays based on quantized ray directions. For example, one embodiment of an apparatus comprises: An apparatus comprising: a ray generator to generate a plurality of rays; ray direction evaluation circuitry/logic to generate approximate ray direction data for each of the plurality of rays; ray sorting circuitry/logic to sort the rays into a plurality of ray queues based, at least in part, on the approximate ray direction data.

Apparatus and method for quantized convergent direction-based ray sorting

An apparatus to facilitate compression of memory data is disclosed. The apparatus comprises one or more processors to receive uncompressed data, adapt a format of the uncompressed data to a compression format, perform a color transformation from a first color space to a second color space, perform a residual computation to generate residual data, compress the residual data via entropy encoding to generate compressed data and packing the compressed data.

Unified memory compression mechanism

Briefly, in accordance with one or more embodiments, a processor performs a coarse depth test on pixel data, and performs a final depth test on the pixel data. Coarse depth data is stored in a coarse depth cache, and per pixel depth data is stored in a per pixel depth cache. If a result of the coarse depth test is ambiguous, the processor is to read the per pixel depth data from the per pixel depth cache, and to update the coarse depth data with the per pixel depth data if the per pixel depth data has a smaller depth range than the coarse depth data.

Hierarchical depth buffer back annotation

Examples described herein relate to a decompression engine that can request compressed data to be transferred over a memory bus. In some cases, the memory bus is a width that requires multiple data transfers to transfer the requested data. In a case that requested data is to be presented in-order to the decompression engine, a re-order buffer can be used to store entries of data. When a head-of-line entry is received, the entry can be provided to the decompression engine. When a last entry in a group of one or more entries is received, all entries in the group are presented in-order to the decompression engine. In some examples, a decompression engine can borrow memory resources allocated for use by another memory client to expand a size of re-order buffer available for use. For example, a memory client with excess capacity and a slowest growth rate can be chosen to borrow memory resources from.

Programmable re-order buffer for decompression

Briefly, in accordance with one or more embodiments, an apparatus comprises a processor to compute depth values for one or more 4&#xd7;4 blocks of pixels using 16 source interpolators and 8 destination interpolators on an incoming fragment of pixel data if the destination is in min/max format, and a memory to store a depth test result performed on the one or more 4&#xd7;4 blocks of pixels. Otherwise the processor is to compute depth values for one or more 8&#xd7;4 blocks of pixels using 16 source interpolators and 16 destination interpolators if the destination is in plane format.

Low granularity coarse depth test efficiency enhancement

Growing community of inventors

Rancho Cordova, CA, United States of America

Karol A Szerszen