Even with the integration of GPUs and CPUs into the same chip, GPGPU is quite awkward for software developers. The CPU and GPU have their own pools of memory. Physically, these might use the same chips on the motherboard (as most integrated GPUs carve off a portion of system memory for their own purposes). From a software perspective, however, these are completely separate.
This means that whenever a CPU program wants to do some computation on the GPU, it has to copy all the data from the CPU's memory into the GPU's memory. When the GPU computation is finished, all the data has to be copied back. This need to copy back and forth wastes time and makes it difficult to mix and match code that runs on the CPU and code that runs on the GPU.
The need to copy data also means that the GPU can't use the same data structures that the CPU is using. While the exact terminology varies from programming language to programming language, CPU data structures make extensive use of pointers: essentially, memory addresses that refer (or, indeed, point) to other pieces of data. These structures can't simply be copied into GPU memory, because CPU pointers refer to locations in CPU memory. Since GPU memory is separate, these locations would be all wrong when copied.
hUMA is the way AMD proposes to solve this problem. With hUMA, the CPU and GPU share a single memory space. The GPU can directly access CPU memory addresses, allowing it to both read and write data that the CPU is also reading and writing.
hUMA is a cache coherent system, meaning that the CPU and GPU will always see a consistent view of data in memory. If one processor makes a change then the other processor will see that changed data, even if the old value was being cached.
Originally Posted by iPaul
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