Abstract:
A method and apparatus to facilitate shared pointers in a heterogeneous platform. In one embodiment of the invention, the heterogeneous or non-homogeneous platform includes, but is not limited to, a central processing core or unit, a graphics processing core or unit, a digital signal processor, an interface module, and any other form of processing cores. The heterogeneous platform has logic to facilitate sharing of pointers to a location of a memory shared by the CPU and the GPU. By sharing pointers in the heterogeneous platform, the data or information sharing between different cores in the heterogeneous platform can be simplified.
Abstract:
A method and system for shared virtual memory between a central processing unit (CPU) and a graphics processing unit (GPU) of a computing device are disclosed herein. The method includes allocating a surface within a system memory. A CPU virtual address space may be created, and the surface may be mapped to the CPU virtual address space within a CPU page table. The method also includes creating a GPU virtual address space equivalent to the CPU virtual address space, mapping the surface to the GPU virtual address space within a GPU page table, and pinning the surface.
Abstract:
A method and system for sharing memory between a central processing unit (CPU) and a graphics processing unit (GPU) of a computing device are disclosed herein. The method includes allocating a surface within a physical memory and mapping the surface to a plurality of virtual memory addresses within a CPU page table. The method also includes mapping the surface to a plurality of graphics virtual memory addresses within an I/O device page table.
Abstract:
A method and system for sharing memory between a central processing unit (CPU) and a graphics processing unit (GPU) of a computing device are disclosed herein. The method includes allocating a surface within a physical memory and mapping the surface to a plurality of virtual memory addresses within a CPU page table. The method also includes mapping the surface to a plurality of graphics virtual memory addresses within an I/O device page table.
Abstract:
Load balancing may be achieved in heterogeneous computing environments by first evaluating the operating environment and workload within that environment. Then, if energy usage is a constraint, energy usage per task for each device may be evaluated for the identified workload and operating environments. Work is scheduled on the device that maximizes the performance metric of the heterogeneous computing environment.
Abstract:
Load balancing may be achieved in heterogeneous computing environments by first evaluating the operating environment and workload within that environment. Then, if energy usage is a constraint, energy usage per task for each device may be evaluated for the identified workload and operating environments. Work is scheduled on the device that maximizes the performance metric of the heterogeneous computing environment.