Abstract: A processing device includes a sorting module, which adds to each of a plurality of elements a position value of a corresponding position in a register rest resulting in a plurality of transformed elements in corresponding positions. The plurality of elements include a plurality of bits. The sorting module compares each of the plurality of transformed elements to itself and to one another. The sorting module also assigns one of an enabled or disabled indicator to each of the plurality of the transformed elements based on the comparison. The sorting module further counts a number of the enabled indicators assigned to each of the plurality of the transformed elements to generate a sorted sequence of the plurality of elements.

Abstract: In one embodiment, a processor may include a vector unit to perform operations on multiple data elements responsive to a single instruction, and a control unit coupled to the vector unit to provide the data elements to the vector unit, where the control unit is to enable an atomic vector operation to be performed on at least some of the data elements responsive to a first vector instruction to be executed under a first mask and a second vector instruction to be executed under a second mask. Other embodiments are described and claimed.

Abstract: A processor includes a cache, a front end to decode an instruction, execution units to execute the instruction, and a retirement unit to retire the instruction. The instruction specifies that a vector of data will be prefetched. The instruction is to include a mask, the mask is to indicate whether corresponding values of the vector of data are included within the cache. The execution units include logic to issue prefetches for each value of the vector of data for which the mask indicates that is unavailable within the cache, and logic to suppress prefetches for each value of the vector of data for which the mask indicates that is available within the cache.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to optimize sparse matrix execution. An example disclosed apparatus includes a context former to identify a matrix function call from a matrix function library, the matrix function call associated with a sparse matrix, a pattern matcher to identify an operational pattern associated with the matrix function call, and a code generator to associate a function data structure with the matrix function call exhibiting the operational pattern, the function data structure stored external to the matrix function library, and facilitate a runtime link between the function data structure and the matrix function call.

Abstract: In one embodiment, a processor may include a vector unit to perform operations on multiple data elements responsive to a single instruction, and a control unit coupled to the vector unit to provide the data elements to the vector unit, where the control unit is to enable an atomic vector operation to be performed on at least some of the data elements responsive to a first vector instruction to be executed under a first mask and a second vector instruction to be executed under a second mask. Other embodiments are described and claimed.

Abstract: Methods and apparatuses relating to a prefetch instruction to prefetch a multidimensional block of elements from a multidimensional array into a cache. In one embodiment, a hardware processor includes a decoder to decode a prefetch instruction to prefetch a multidimensional block of elements from a multidimensional array into a cache, wherein at least one operand of the prefetch instruction is to indicate a system memory address of an element of the multidimensional block of elements, a stride of the multidimensional block of elements, and boundaries of the multidimensional block of elements, and an execution unit to execute the prefetch instruction to generate system memory addresses of the other elements of the multidimensional block of elements, and load the multidimensional block of elements into the cache from the system memory addresses.

Abstract: A processor includes a front end with a decoder with logic to identify a calculation instruction associated with a vector read. The processor also includes an execution unit with logic to issue a request for an address, the request to be implemented with the vector read. The processor further includes a cache and an alignment unit with logic to determine that the address is unaligned with the vector read, and to, based upon the determination that the address is unaligned with the vector read, determine whether to select successive cachelines from the cache or from an alignment buffer, the cacheline to include the address.

Abstract: A region or group of pixels may be textured as a unit, using a range specifier and one or more anchor pixels to define the group. In some embodiments, processing grouped pixels improves efficiency.

Abstract: A texture unit may be used to perform general purpose mathematical computations such as dot products. This enables some general purpose computations and operations to be offloaded from a central processing unit to the texture unit. The texture unit may use linear interpolators in order to perform the dot product calculations.

Abstract: A texture unit may be used to perform general purpose mathematical computations such as dot products. This enables some general purpose computations and operations to be offloaded from a central processing unit to the texture unit. The texture unit may use linear interpolators in order to perform the dot product calculations.

Abstract: A texture unit may be used to perform general purpose mathematical computations such as dot products. This enables some general purpose computations and operations to be offloaded from a central processing unit to the texture unit. The texture unit may use linear interpolators in order to perform the dot product calculations.

Abstract: A system and method are configured to detect conflicts when converting scalar processes to parallel processes (“SIMDifying”). Conflicts may be detected for an unordered single index, an ordered single index and/or ordered pairs of indices. Conflicts may be further detected for read-after-write dependencies. Conflict detection is configured to identify operations (i.e., iterations) in a sequence of iterations that may not be done in parallel.

Abstract: In one embodiment, a processor may include a vector unit to perform operations on multiple data elements responsive to a single instruction, and a control unit coupled to the vector unit to provide the data elements to the vector unit, where the control unit is to enable an atomic vector operation to be performed on at least some of the data elements responsive to a first vector instruction to be executed under a first mask and a second vector instruction to be executed under a second mask. Other embodiments are described and claimed.

Abstract: A texture unit may be used utilized to perform general purpose mathematical computations such as dot products. This enables some general purpose computations and operations to be offloaded from a central processing unit to the texture unit. The texture unit may use linear interpolators in order to perform the dot product calculations.

Abstract: Parallel and vectored data structures may be used in a single instruction multiple data processor that applies the Gilbert-Johnson-Keerthi algorithm. As a result, the performance of multi-core processors doing graphics processing may be increased in some cases.

Abstract: A system and method are configured to detect conflicts when converting scalar processes to parallel processes (“SIMDifying”). Conflicts may be detected for an unordered single index, an ordered single index and/or ordered pairs of indices. Conflicts may be further detected for read-after-write dependencies. Conflict detection is configured to identify operations (i.e., iterations) in a sequence of iterations that may not be done in parallel.

Abstract: A region or group of pixels may be textured as a unit, using a range specifier and one or more anchor pixels to define the group. In some embodiments, processing grouped pixels improves efficiency.

Abstract: Parallel and vectored data structures may be used in a single instruction multiple data processor that applies the Gilbert-Johnson-Keerthi algorithm. As a result, the performance of multi-core processors doing graphics processing may be increased in some cases.

Abstract: A texture unit may be used utilized to perform general purpose mathematical computations such as dot products. This enables some general purpose computations and operations to be offloaded from a central processing unit to the texture unit. The texture unit may use linear interpolators in order to perform the dot product calculations.

Abstract: An interpolation unit, such as may be found in a texture unit or texture sampler, may be used utilized to perform general purpose mathematical computations such as dot products. This enables some general purpose computations and operations to be offloaded from a central processing unit to an interpolation unit. The interpolation unit may use linear interpolators in order to perform the dot product calculations.