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One common pitfall with many cheap blog posts describing why GPUs are "fast" is the focus on the number of CUDA cores. The author here does a great job at high

• anonymousDan 6y ago
  When you say the memory bandwidth makes a big difference, you mean GPUs have more memory bandwidth than CPUs? My (rudimentary) understanding was that CPU/GPU memory bandwidth is often a bottleneck - is that not correct? Or are you referring instead to the aggregate memory bandwidth between GPU cores and local GPU memory?
  • freeqaz 6y ago
    You’re right that PCI-E bandwidth is the bottleneck for CPU-to-GPU communication. Game devs often have to think in “number of draw calls” being sent (especially before DX12/Vulcan). You can easily saturate that channel. I believe what OP is talking about, in the context of ML models, is that reading 1 byte of memory on the GPU from the GPU’s memory is _much_ slower than the CPU reading from the system RAM. This is an intentional choice and it speaks to the core design of what each system is solving for. CPUs trade low latency for faster single threaded execution speed. GPUs trade high latency for “total thread” execution speed. CPUs solve for maximum “single thread” performance (for lack of a better term). If you have an operation that reads and mutates one byte of RAM, and then stack many of those instructions into a long sequence, the CPU is very fast at executing that. Most programs we write do this. Processing the steps for a single-thread of an application. GPUs optimize for concurrency and they do that by running many “threads”. Each thread is often memory bound, but because they run in parallel when one “blocks” on reading memory, another thread just pops in it’s place until it has to block. GPUs are constantly swapping active “threads” running and are able to hide the latency better. And, because of that design, you can trade for higher bandwidth and get more instructions out. Which, for image data (what GPUs were originally designed for), you’re manipulating big two dimensional arrays of pixel data where concurrency is important. CPUs have instructions like SSE/AVX that sit somewhere in between, these days, but GPUs have the advantage of being able to target only one domain instead of both. That’s my understanding of it, at least. I was a game dev in a former life :)
    • p_l 6y ago
      Also memory access patterns on GPU are more like batch access - you set up parameters for a large transfer then stream it with high bandwidth (texture, VBO, FBO, etc. etc.)
    • NotCamelCase 6y ago
      Number of drawcalls is usually not a problem because it saturates memory, though. It is often because of CPU overhead it incurs and (possibly redundant) commands-processing work on GPUs front-end causing bubbles in the whole pipeline. Where DX12/Vulkan-esque APIs help immensely is the first part mostly -- CPU overhead. Memory interfacing of GPU commands-streaming/processing front-ends and system memory are very efficient, employing pre-fetching, etc.
      • freeqaz 6y ago
        Ah, that makes sense. Thank you for clarifying!
      • fulafel 6y ago
        You can also easily see this in game benchmarks, eg in https://www.gamersnexus.net/guides/2488-pci-e-3-x8-vs-x16-performance-impact-on-gpus https://www.gamersnexus.net/guides/2488-pci-e-3-x8-vs-x16-pe... "From a quick look, there is a little below a 1% [game FPS] difference in PCI-e 3.0 x16 and PCI-e 3.0 x8 slots". Also an important thing to remember is that the vast majority of GPUs (and GPU end-users) out there are iGPUs that share memory with the host and can also be programmed to take advantage of this fact.
    • willis936 6y ago
      PCIe 3.0 x16 is a 16 GB/s link, which ain’t bad. By comparison, CPU dual channel DDR4-2400 main memory is 38.4 GB/s. All processors are memory bandwidth starved. CPUs don’t benefit from wide, high latency access to large main memory as much as GPUs, due to the nature of SISD vs. SIMD. Naturally, GPUs put more focus on a fatter pipe between the processor and main memory (GDDR and 500 GB/s pipes). CPUs operate on fewer data, so you can crank the speed of computation up if you crank the memory speed and keep latency low. This is why so much of CPU dies are dedicated to cache (I think L1 is often single cycle, which results in absurd throughout numbers).
  • fxtentacle 6y ago
    CPU memory to CPU cores is medium speed. That's why the CPU cores have caches, to make it faster. CPU memory to GPU memory is slow. The other direction is even slower. But GPU memory to GPU processing cores is insanely fast if you stream large amounts of homogeneous data.
    • BubRoss 6y ago
      PCIe 4 bandwidth is around 32 GB/s. Most games or applications don't saturate PCIe 3 16 lane bandwidth. I think you are conflating latency, bandwidth and "speed".
  • arnon 6y ago
    Yes. Memory-intensive operations like decompression and sorting will benefit greatly from running on a high memory bandwdith infrastructure. I think the comparison will be clearer when I put these two side-by-side: * NVIDIA Tesla V100. Maximum memory bandwidth: 900GB/s to the HBM2 memory * Intel Xeon Platinum 8180. Maximum memory bandwidth: 119.21 GiB/s to DDR4-2666 at hexa-channel.
• amelius 6y ago
  GPU architecture newbie here. Why isn't the memory interwoven with the cores, so you have faster local access and more streamlike data flow like in a systolic array?
  • p_l 6y ago
    Because you want to use COTS chips for memory. Cost of interleaving memory and compute is huge, and only gets worse when you consider the fact that such memory is "locked" to particular product. So the only interleaved memory is very small caches local to the compute cores. (And I'm not even getting into power and thermals involved)
  • NotCamelCase 6y ago
    Why aren't there e.g. 16 GB of L1D$ inside our CPUs? It'd be blazingly fast! :) Although memory access patterns of typical GPU workloads are very different than those of CPU workloads, there are GPU-local, hierarchical caches similar to CPUs for different purposes that are exclusive to shader cores/computation units, which can be even partitioned dynamically. The struggle on the GPU side is keeping all the cores busy while servicing memory requests efficiently. Sharing memory across small group of threads run in lockstep, for example, is a great way of doing it.
    • amelius 6y ago
      > Why aren't there e.g. 16 GB of L1D$ inside our CPUs? It'd be blazingly fast! :) I didn't say the memory necessarily has to be moved into the GPU. The other way around would also be a possibility.
      • NotCamelCase 6y ago
        Yep, that's not what I intended to hint at, either. What I mean is, it boils down to the familiar problem of diminishing returns to have huge caches inside GPUs.
  • jabl 6y ago
    I'm not an expert in this area, but my understanding is that DRAM and logic processes are very different from each other. If you want both DRAM and logic on the same chip you'll end up with a compromise which isn't that good for either. Which, aside from the programmability issue, is why processor-in-memory (PIM) approaches haven't caught on (yet). Latest generation high end GPU's use HBM2(E?) memory, which is a very wide and fast pipe compared to DDR4 used for "normal" CPU main memory. As for systolic arrays, to some extent the matrix-matrix units in Google TPU's and NVIDIA Tensor Cores are systolic arrays. I suspect we'll see designs go further down that path in the future.
• BubRoss 6y ago
  Memory bandwidth keeps the cores from being bottlenecked, but saying it is the reason GPUs are fast is simplistic.