Maintaining workload data coherency between local and remote accelerators

One or more non-transitory machine-readable media storing instructions for being executed by one or more machines, the one or more machines to be associated with a cloud computing system, the cloud computing system being for use in providing at least one service associated with at least one workload, execution of the at least one workload being configurable to be associated with machine-learning operations, the instructions, when executed by the one or more machines, resulting in the cloud computing system being configurable to perform certain operations comprising:

• configuring compute resources of the cloud computing system to comprise at least one central processing unit and memory circuitry;
• configuring accelerator resources of the cloud computing system to comprise graphics accelerator circuitry and/or field programmable gate array circuitry;
• and
• configuring storage resources of the cloud computing system for use in association with the compute resources and/or the accelerator resources;
• wherein:
• the cloud computing system is to dynamically allocate and/or reallocate, at least in part, based upon resource utilization prediction data, the compute resources, the accelerator resources, and/or the storage resources for use in the execution of the at least one workload;
• the accelerator resources are configurable for use in implementing the machine-learning operations;
• the execution of the at least one workload is to result in modified workload-related data;
• and
• the compute resources are configurable to comprise local and remote compute resources;
• the accelerator resources are configurable to comprise local and remote accelerator resources;
• and
• the cloud computing system is configurable, based upon workload configuration request data, to coordinate providing of provide the modified workload-related data between the local and the remote compute resources and/or the local and the remote accelerator resources to maintain data coherency with regard to the workload-related data.

A method implemented in association with a cloud computing system, the cloud computing system being for use in providing at least one service associated with at least one workload, execution of the at least one workload being configurable to be associated with machine-learning operations, the method comprising:

• configuring compute resources of the cloud computing system to comprise at least one central processing unit and memory circuitry;
• configuring accelerator resources of the cloud computing system to comprise graphics accelerator circuitry and/or field programmable gate array circuitry;
• and
• configuring storage resources of the cloud computing system for use in association with the compute resources and/or the accelerator resources;
• wherein:
• the cloud computing system is to dynamically allocate and/or reallocate, at least in part, based upon resource utilization prediction data, the compute resources, the accelerator resources, and/or the storage resources for use in the execution of the at least one workload;
• the accelerator resources are configurable for use in implementing the machine-learning operations;
• the execution of the at least one workload is to result in modified workload-related data;
• the compute resources are configurable to comprise local and remote compute resources;
• the accelerator resources are configurable to comprise local and remote accelerator resources;
• and
• the cloud computing system is configurable, based upon workload configuration request data, to coordinate providing of the modified workload-related data between the local and the remote compute resources and/or the local and the remote accelerator resources to maintain data coherency with regard to the workload-related data.