"workload sharing" is a correct and usable phrase in written English.
It refers to the distribution of tasks or responsibilities among a group of people in order to lighten the workload for each individual. Example: In order to prevent burnout and maintain a healthy work-life balance, the company implemented a system of workload sharing among its employees. Each team member is assigned specific tasks and rotates them among the team throughout the week. This has led to increased productivity and a more cooperative work environment.
The optimality criterion concerns the equity of the workload sharing.
The most common usage is in-house (intra-organisational or intra-institutional) distribution of imaging studies, as a tool for efficient workload sharing between the different locations of the same organisation (71%%).
"More integrated teamwork, proper workload sharing with nurses and others is needed; efficient management, coordination, and mutual support rarely exist" (LT,F,35).
Important systems concerns unique to the distributed case are workload sharing, which attempts to take advantage of access to multiple computers to complete jobs faster; task migration, which supports workload sharing by efficiently moving jobs among machines; and automatic task replication at different sites for greater reliability.
All APs need to synchronize by using beacon packets, and workload sharing or weighted fair sharing are all dependent on message passing among the APs.
The parallel table function works best for general queries with high volumes of data being processed and many positive verifications being returned, the VF2 workload shared over several slave processes.
Under less stressful workloads, sharing both L2 and L3 caches have the upper hand.
Experimental results for energy prediction demonstrate that we can accurately predict the peak energy consumption of an application on a target platform; whereas, results for energy optimization indicate that with no a priori knowledge of workloads sharing the platform we can save up to 24% of the overall HPC system's energy consumption under benchmarks and real-life workloads.
When multiple workloads share a single GPU device at fine-grained level (e.g., API-level multiplexing [ 20– 22]), the GPU memory should be shared between multiple workloads.
In addition, we leverage the generating easy questions and finding target workers to improve the quality of evaluating and reduce the workers' workload, which share the same motivation of some research works of Crowdsourcing question designing and workers selecting.
So I guess it's also depending on how workload is shared I think you do need to define who will be responsible for checking progress, or a result or reviewing a patient, and that's a shared responsibility.
