Abstract: Apparatuses, systems, and techniques to perform an operation to indicate one or more non-zero values within one or more matrices of data; to perform an API to compress one or more matrices of data; to perform a matrix multiply accumulate (MMA) operation on two or more matrices of data, wherein at least one of the two or more matrices contain compressed data; and/or to perform an API to decompress one or more matrices of data. In at least one embodiment, one or more circuits are configured to receive and compile one or more instructions to perform computational operations for a sparse matrix multiplication.

Abstract: Apparatuses, systems, and techniques to perform an operation to indicate one or more non-zero values within one or more matrices of data; to perform an API to compress one or more matrices of data; to perform a matrix multiply accumulate (MMA) operation on two or more matrices of data, wherein at least one of the two or more matrices contain compressed data; and/or to perform an API to decompress one or more matrices of data. In at least one embodiment, one or more circuits are configured to receive and compile one or more instructions to perform computational operations for a sparse matrix multiplication.

Abstract: Apparatuses, systems, and techniques to perform an operation to indicate one or more non-zero values within one or more matrices of data; to perform an API to compress one or more matrices of data; to perform a matrix multiply accumulate (MMA) operation on two or more matrices of data, wherein at least one of the two or more matrices contain compressed data; and/or to perform an API to decompress one or more matrices of data. In at least one embodiment, one or more circuits are configured to receive and compile one or more instructions to perform computational operations for a sparse matrix multiplication.

Abstract: Apparatuses, systems, and techniques to perform an operation to indicate one or more non-zero values within one or more matrices of data; to perform an API to compress one or more matrices of data; to perform a matrix multiply accumulate (MMA) operation on two or more matrices of data, wherein at least one of the two or more matrices contain compressed data; and/or to perform an API to decompress one or more matrices of data. In at least one embodiment, one or more circuits are configured to receive and compile one or more instructions to perform computational operations for a sparse matrix multiplication.

Abstract: The present invention enables efficient pre-fetching of instructions. The present invention novelly determines a location for insertion of pre-fetch instructions earlier than in the past and in a cost effective manner. Therefore, the present invention introduces more control into the determination of when to initiate instruction pre-fetching than in the past. The present invention pre-fetches instructions accurately and launches instructions early enough to avoid cache miss latency. Also the present invention enables pre-fetching of instructions with the appropriate coverage. The present invention novelly generates pre-fetch instructions that have improved coverage over pre-fetching of the past by testing if the probability of a pre-fetch is cost effective and by determining whether the predicted size of a pre-fetched trace supports cost effective pre-fetching.

Abstract: There are two separate, yet related, prefetching strategies used for data references used having multiple strides, which typically occur in data references within nested loop structures. The first approach attempts to reverse one or more of the nested loops so that the strides of the reference are in the same direction. Once the loop or loops are reversed, data elements can be prefetched in the common loop direction. Preferably, the inner loops are reversed as compared with the outer loops, but this is not essential. The second approach is used where the data reference has multiple strides and the loops cannot be reversed. In this case, the prefetching method prefetches in the opposite direction of the innermost loop that surrounds the data reference. The second approach is used when the first approach cannot be used and where the strides of the reference have different directions and the inner loop is expected to iterate relatively few times.