Abstract: Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to indicate a maximum number of blocks of threads to be scheduled in parallel.

Abstract: Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to indicate a maximum number of blocks of threads capable of being scheduled in parallel.

Abstract: Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to indicate one or more limitations of one or more attributes of one or more groups of blocks of one or more threads.

Abstract: Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to cause performance of one or more threads within a group of blocks of threads to stop at least until all threads within the group of blocks have performed a barrier instruction.

Abstract: Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to indicate whether one or more threads within two or more blocks of threads have performed a barrier instruction.

Abstract: Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to indicate whether one or more threads within a group of blocks of threads have performed a barrier instruction and to cause performance of one or more threads within the group of blocks of threads to stop at least until all threads within the group of blocks have performed the barrier instruction.

Abstract: Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to indicate one or more attributes of one or more groups of blocks of one or more threads.

Abstract: Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to cause memory to be shared between two or more groups of blocks of threads.

Abstract: Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to determine a scheduling policy of one or more blocks of one or more threads.

Abstract: Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to cause a kernel to be generated to cause two or more blocks of two or more threads to be scheduled in parallel.

Abstract: Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to determine which of two or more blocks of threads are to be scheduled in parallel.

Abstract: Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to indicate two or more blocks of threads to be scheduled in parallel.

Abstract: Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to indicate a scheduling policy of one or more blocks of one or more threads.

Abstract: Apparatuses, systems, and techniques to cause data to be selectively stored in one or more memory locations. In at least one embodiment, a processor is to cause data to be selectively stored in one or more memory locations based, at least in part, on one or more threads to use the data.

Abstract: Apparatuses, systems, and techniques to indicate contextual information to be used by available logical processors. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to indicate a first set of contextual information to be used by a first subset of available processors.

Abstract: Apparatuses, systems, and techniques to facilitate data retrieval. In at least one embodiment, an application programming interface is used to facilitate indication of a data location and to cause data to be retrieved from the location.

Abstract: Apparatuses, systems, and techniques to generate numbers. In at least one embodiment, one or more circuits are to cause one or more thirty-two bit floating point numbers to be truncated to generate one or more rounded numbers based, at least in part, on one or more rounding attributes.

Abstract: Apparatuses, systems, and techniques to control operation of a memory cache. In at least one embodiment, cache guidance is specified within application source code by associating guidance with declaration of a memory block, and then applying specified guidance to source code statements that access said memory block.

Abstract: A system and method for processing source code for compilation. The method includes accessing a portion of host source code and determining whether the portion of the host source code comprises a device lambda expression. The method further includes in response to the portion of host code comprising the device lambda expression, determining a unique placeholder type instantiation based on the device lambda expression and modifying the device lambda expression based on the unique placeholder type instantiation to produce modified host source code. The method further includes sending the modified host source code to a host compiler.

Abstract: A system and method for optimizing multiple invocations of a graphics processing unit (GPU) program in Java. In one embodiment, the system includes: (1) a frontend component in a computer system and configured to compile Java bytecode associated with the a class object that implements a functional interface into Intermediate Representation (IR) code and store the IR code with the associated jogArray and (2) a collector/composer component in the computer system, associated with the frontend and configured to traverse a tree containing the multiple invocations from the result to collect the IR code and compose the IR code collected in the traversing into aggregate IR code when a result of the GPU program is explicitly requested to be transferred to a host.