Abstract: Techniques are disclosed relating to operand routing among SIMD pipelines. In some embodiments, an apparatus includes a set of multiple hardware pipelines configured to execute a single-instruction multiple-data (SIMD) instruction for multiple threads in parallel, wherein the instruction specifies first and second architectural registers. In some embodiments, the pipelines include execution circuitry configured to perform operations using one or more pipeline stages of the pipeline. In some embodiments, the pipelines include routing circuitry configured to select, based on the instruction, a first input operand for the execution circuitry from among: a value from the first architectural register from thread-specific storage for another pipeline and a value from the second architectural register from thread-specific storage for a thread assigned to another pipeline.

Abstract: Method, apparatus, and program means for nonlinear filtering and deblocking applications utilizing SIMD sign and absolute value operations. The method of one embodiment comprises receiving first data for a first block and second data for a second block. The first data and said second data are comprised of a plurality of rows and columns of pixel data. A block boundary between the first block and the second block is characterized. A correction factor for a deblocking algorithm is calculated with a first instruction for a sign operation that multiplies and with a second instruction for an absolute value operation. Data for pixels located along said block boundary between the first and second block are corrected.

Abstract: Embodiments of systems, apparatuses, and methods for performing in a computer processor vector double block packed sum of absolute differences (SAD) in response to a single vector double block packed sum of absolute differences instruction that includes a destination vector register operand, first and second source operands, an immediate, and an opcode are described.

Abstract: A method provides support for packed sum of absolute difference operations in a floating point execution unit, e.g., a scalar or vector floating point execution unit. Existing adders in a floating point execution unit may be utilized along with minimal additional logic in the floating point execution unit to support efficient execution of a fixed point packed sum of absolute differences instruction within the floating point execution unit, often eliminating the need for a separate vector fixed point execution unit in a processor architecture, and thereby leading to less logic and circuit area, lower power consumption and lower cost.

Abstract: Domain-based cache management methods and systems, including domain event based priority demotion (“EPD”). In EPD, priorities of cached data blocks are demoted upon one or more domain events, such as upon encoding of one or more macroblocks of a video frame. New data blocks may be written over lowest priority cached data blocks. New data blocks may initially be assigned a highest priority. Alternatively, or additionally, one or more new data blocks may initially be assigned one of a plurality of higher priorities based on domain-based information, such as a relative position of a requested data block within a video frame, and/or a relative direction associated with a requested data block. Domain-based cache management may be implemented with one or more other cache management techniques, such as least recently used techniques. Domain-based cache management may be implemented in associative caches, including set associative caches and fully associative caches, and may be implemented with indirect indexing.