Automated resource usage configurations for deep learning neural network workloads on multi-generational computing architectures

A semiconductor package comprising:

• one or more substrates;
• and
• logic coupled to the one or more substrates, wherein the logic is implemented at least partly in one or more of configurable logic or fixed-functionality hardware logic, the logic coupled to the one or more substrates to:
• identify a first set of compute nodes and a second set of compute nodes, wherein the first set of compute nodes execute more slowly than the second set of compute nodes,
• determine a compute node configuration that results in a relatively low difference in completion time between the first set of compute nodes and the second set of compute nodes with respect to a neural network workload, and
• apply the compute node configuration to an execution of the neural network workload on one or more nodes in the first set of compute nodes and one or more nodes in the second set of compute nodes.

At least one computer readable storage medium comprising a set of executable program instructions, which when executed by a computing system, cause the computing system to:

• identify a first set of compute nodes and a second set of compute nodes, wherein the first set of compute nodes execute more slowly than the second set of compute nodes;
• determine a compute node configuration that results in a relatively low difference in completion time between the first set of compute nodes and the second set of compute nodes with respect to a neural network workload;
• and
• apply the compute node configuration to an execution of the neural network workload on one or more nodes in the first set of compute nodes and one or more nodes in the second set of compute nodes.

A method comprising:

• identifying a first set of compute nodes and a second set of compute nodes, wherein the first set of compute nodes execute more slowly than the second set of compute nodes;
• determining a compute node configuration that results in a relatively low difference in completion time between the first set of compute nodes and the second set of compute nodes with respect to a neural network workload;
• and
• applying the compute node configuration to an execution of the neural network workload on one or more nodes in the first set of compute nodes and one or more nodes in the second set of compute nodes.