Heterogeneous Computing. Mohamed Zahran
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Название: Heterogeneous Computing
Автор: Mohamed Zahran
Издательство: Ingram
Жанр: Компьютерное Железо
Серия: ACM Books
isbn: 9781450360982
isbn:
Figure 2.3Generic GPU Design
The confusing thing about GPUs is that each brand has its own naming convention. In NVIDIA parlance, those blocks are called streaming multiprocessors (SM or SMX in later version) and the execution units are called streaming processors (SPs) or CUDA cores. In AMD parlance those blocks are called shader engines and the execution units are called compute units. In Intel parlance, the blocks are called slices (or sub-slices) and the execution units are called just like that: execution units. There are some very slight differences between each design, but the main idea is almost the same.
GPUs can be discrete, that is, stand-alone chips connected to the other processors using connections like PCIe or NVLink, or they can be embedded with the multicore processor in the same chip. On the one hand, the discrete ones are of course more powerful because they have more real estate. But they suffer from the communication overhead of sending the data back and forth between the GPU’s memory and the system’s memory [Jablin et al. 2011], even if the programmer sees a single virtual address space. On the other hand, the embedded GPUs, like Intel GPUs and many AMD APUs, are tightly coupled with the multicore and do not suffer from communication overhead. However, embedded GPUs have limited area because they share the chip with the processor and hence are weaker in terms of performance.
If you have a discrete GPU in your system, there is a high chance you also have an embedded GPU in your multicore chip, which means you can make use of a multicore processor, an embedded GPU, and a discrete GPU, which is a nice exercise of heterogeneous programming!
Let’s see an example of a recent GPU: the Volta architecture V100 from NVIDIA [2017]. Figure 2.4 shows the block diagram of the V100. The giga thread engine at the top of the figure is what we called high-level scheduling in our generic GPU of Figure 2.3. Its main purpose is to schedule blocks to SMs. A block, in NVIDIA parlance, is a group of threads, doing the same operations on different data, assigned to the same SM, so that they can share data more easily and synchronize. There is an L2 cache shared by all, and it is the last-level cache (LLC) before going off-chip to the GPU global memory, not shown in the figure. NVIDIA packs several SMs together in what are called GPU processing clusters (GPCs). In Volta there are six GPCs; each one has 14 SMs. You can think of a GPC as a small full-fledged GPU, with its SMs, raster engines, etc. The main players, who actually do the computations, are the SMs.
Figure 2.4NVIDIA V100 GPU block diagram. (Based on NVIDIA, 2017. NVIDIA Tesla v100 GPU architecture)
Figure 2.5NVIDIA V100 GPU streaming multiprocessor. (Based on NVIDIA, 2017. NVIDIA Tesla v100 GPU architecture)
Figure 2.5 shows the internal configuration of a single SM. Each SM is equipped with an L1 data cache and a shared memory. The main difference is that the cache is totally transparent to the programmer. The shared memory is controllable by the programmer and can be used to share data among the block of threads assigned to that SM. This is why a block of threads assigned to the same SM can share data faster. There is also an L1 instruction cache and an L0 instruction cache for each warp scheduler. Warp? What is a warp?
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