CPU & GPU - The Basics - A digestible high-level overview of what happens in The Die
A digestible high-level overview of what happens in The Die In this article, we'll go through some fundamental low-level details to understand why GPUs are good at Graphics, Neural Networks, and Deep Learning tasks and CPUs are good at a wide number of sequential, complex general purpose computing tasks. There were several topics that I had to research and get a bit more granular understanding for this post, some of which I will be just mentioning in passing.
A100 GPU. One thing that was surprising to me while researching for this article was that CPU vendors don't publish how many ALUs, FPUs, etc., are available in execution units of a core. programmers understand this very well. Multiple Streaming Multiprocessors Multi-Core CPU Streaming Multiprocessor CPU Core Streaming processor /
cores can done only one 1x1 FP64 MMA where as Tensor cores can do 4x4 FP64 MMA instruction per clock cycle. Key Takeaways High number of compute units , high number of threads and registers , reduced instruction set, no L3 cache, HBM , simple & high throughput memory access pattern are the principles that make GPUs so much better than CPUs in parallel computing Beyond GPUs GPUs were first created for handling graphics processing tasks.
has been increasing the number of Tensor cores in each architecture. But, these GPUs are also good at graphics processing. Although the instruction set and complexity is much less in GPUs, it's not fully dedicated to deep learning . FlashAttention 2, a software layer optimization for transformer architecture provides 2X speedup in tasks.
cores, 432 FP 64 Tensor cores, 7344 hardware threads Pipeline threads per SM: 2048 - 68=1980 per SM Overall pipeline threads per GPU: - =21184 - 7344=13840 Refer: cudaLimitDevRuntimePendingLaunchCount L2 Cache: 40 MB L1 Cache: 20.3 MB in total Register size: 27.8 MB Max GPU Main Memory: 80GB HBM2e, 1512 MHz Max GPU Main Memory Bandwidth: 2.39 TB/s Peak FP64 Performance=19.5 TFLOPs . The lower value of 9.
cores are used. This number is a theoretical max limit which means, FP64 circuits are being used to their fullest. has 1065 MHz base and 1410 MHz at Turbo 108 SMs, 64 FP32 cores per SM, 4 FP64 Tensor cores per SM, 68 hardware threads per SM Overall per GPU: 6912 64 FP32 cores are used. This number is a theoretical max limit which means, FP64 circuits are being used to their fullest. Core in a modern GPU Terminologies we saw in CPU don't always translate directly to GPUs. Here we'll see components and core
programming, so comparing it with CPU equivalents helps with initial understanding. programming model & "batching" in model serving optimization technique where we can see how beneficial this is. The above diagram depicts hardware thread execution in the CPU & GPU core. Refer "memory access" section we discussed earlier in CPU pipelining. This diagram shows that. CPUs complex memory management makes this wait time small enough to fetch data from the L1 cache to registers.
cores.
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