原始名称记载: ADVANCED MICRO DEVICES, INC.

A processing system identifies both the types of errors detected at a memory and the severity of the errors. The processing system keeps track of errors in error logs. The processing system employs a fault analyzer to generate, based on the error logs, a recommended management solution for one or more of the detected errors.

A direct memory access (DMA) controller issuing memory copy operations on behalf of a shader at a parallel processor stops issuing copy operations upon a context switch at the shader for a wave. The DMA controller or a trap handler associated with the shader saves the incomplete copy operations to a region of global memory, from which the incomplete operations are restored upon a context resume for the wave.

A processor includes a plurality of processing elements. Each processing element is configured to obtain a first plurality of submatrices from a first input matrix and a second plurality of submatrices from a second input matrix. The first and second plurality of submatrices, for at least a first iteration of a plurality of matrix multiply iterations, each include at least one submatrix that is distinct from submatrices obtained by the other processing elements. The processing element performs one or more matrix multiplication operations on the first plurality of submatrices and the second plurality of submatrices to generate partial results for an output submatrix of an output matrix associated with the processing element. The processing element generates a portion of the output matrix by combining the partial results in the memory for the output submatrix. The output submatrices generated by each of the processing elements form the output matrix.

To tune a machine-learning model for a deployment system, a machine-learning model training system generates multiple tuning steps for the machine-learning model and generates an accuracy loss sensitivity for each tuning step. Each of the tuning steps indicate a corresponding set of one or more parameters and hyperparameters that reduces the impact of the machine-learning model on the system performance of the deployment system. Based on the accuracy loss sensitivities, the machine-learning model training system selects a tuning step with the least impact on the accuracy of the machine-learning model and modifies the machine-learning model based on the selected tuning step. After also modifying the tuned machine-learning model based on a threshold accuracy, the machine-learning model training system provides the tuned machine-learning model to the deployment system.