Maintaining workload data coherency between local and remote accelerators
Technologies for composing a managed node with multiple processors on multiple compute sleds to cooperatively execute a workload include a memory, one or more processors connected to the memory, and an accelerator. The accelerator further includes a coherence logic unit that is configured to receive a node configuration request to execute a workload. The node configuration request identifies the compute sled and a second compute sled to be included in a managed node. The coherence logic unit is further configured to modify a portion of local working data associated with the workload on the compute sled in the memory with the one or more processors of the compute sled, determine coherence data indicative of the modification made by the one or more processors of the compute sled to the local working data in the memory, and send the coherence data to the second compute sled of the managed node.
1 . A cloud computing system for use in providing at least one service associated with executing at least one workload, execution of the at least one workload being configurable to be associated with machine-learning operations, the cloud computing system comprising:
compute resources comprising at least one central processing unit and memory circuitry;
accelerator resources comprising graphics accelerator circuitry and/or field programmable gate array circuitry; and
storage resources 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.
2 . The cloud computing system of claim 1 , wherein:
the at least one workload comprises at least one virtual machine-based workload and/or at least one container-based workload.
3 . The cloud computing system of claim 1 , wherein:
the cloud computing system is configurable to allocate network data communication bandwidth and/or quality of service associated with the execution of at least one workload.
4 . The cloud computing system of claim 1 , wherein:
the cloud computing system is configurable to dynamically allocate and/or reallocate, based upon resource utilization data, the compute resources, the accelerator resources, and/or the storage resources for use in the execution of the at least one workload.
5 . The cloud computing system of claim 1 , wherein:
the cloud computing system is comprised in multiple data centers.
6 . The cloud computing system of claim 5 , wherein:
the multiple data centers comprise multiple rack servers.
7 . 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.
8 . The one or more non-transitory machine-readable media of claim 7 , wherein:
the at least one workload comprises at least one virtual machine-based workload and/or at least one container-based workload.
9 . The one or more non-transitory machine-readable media of claim 7 , wherein:
the cloud computing system is configurable to allocate network data communication bandwidth and/or quality of service associated with the execution of at least one workload.
10 . The one or more non-transitory machine-readable media of claim 7 , wherein:
the cloud computing system is configurable to dynamically allocate and/or reallocate, based upon resource utilization data, the compute resources, the accelerator resources, and/or the storage resources for use in the execution of the at least one workload.
11 . The one or more non-transitory machine-readable media of claim 7 , wherein:
the cloud computing system is comprised in multiple data centers.
12 . The one or more non-transitory machine-readable media of claim 11 , wherein:
the multiple data centers comprise multiple rack servers.
13 . 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.
14 . The method of claim 13 , wherein:
the at least one workload comprises at least one virtual machine-based workload and/or at least one container-based workload.
15 . The method of claim 13 , wherein:
the cloud computing system is configurable to allocate network data communication bandwidth and/or quality of service associated with the execution of at least one workload.
16 . The method of claim 13 , wherein:
the cloud computing system is configurable to dynamically allocate and/or reallocate, based upon resource utilization data, the compute resources, the accelerator resources, and/or the storage resources for use in the execution of the at least one workload.
17 . The method of claim 13 , wherein:
the cloud computing system is comprised in multiple data centers.
18 . The method of claim 17 , wherein:
the multiple data centers comprise multiple rack servers.