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Edit: If it uses the same cores as MI200 will use CDNA2 cores. Unique Instructions for AI Training, AI Inference, & HPC Datacenter GPUs support the latest TF32, BFLOAT16, FP64 Tensor Core, and Int8 instructions that dramatically improve application performance. AMD used to do something similar through their extension cl_amd_fp64. The MI200 is anticipated to deliver at least twice the performance of the single-die, 11.5-teraflops MI100, which had 120 compute units and 32GB of HBM2 memory. At up to 600GB/sec per GPU, your data moves freely throughout the system and nearly 20X the rate of PCI-E x16 3.0 GPUs. I have a feature request that perhaps Intel can release limited fp64 support through a vendor extensions (something like cl_intel_fp64) where a subset of cl_khr_fp64 can be supported. Thus, it would have been great if we could use fp64 on HD graphics through OpenCL. However, the majority of the use-cases of my compiler are in scientific computing where people tend to use fp64 a lot. The GPU has a clock speed configured around 1500 MHz and delivers a peak performance throughput of 11.5 TFLOPs in FP64, 23.1 TFLOPs in FP32, and a massive 185 TFLOPs in FP16 compute workloads. They benefit from not having to do data transfers in many cases. They are very suitable for the case where both the CPU and the GPU cores work on different parts of the problem, rather than a pure offload model. With Haswell GT3e, I think the HD graphics GPU now has sufficient processing power and bandwidth.
#Half rate fp64 gpu code
Which means in an ideal case, running the same code by only changing float types to double types, would yield the single precision run time to be about 1/24th of the double precision time (time(FP32) time(FP64)/24). Overall, AMD GPUs hold a reputation for good double precision performance ratios compared to their NVIDIA counterparts. The FirePro W9100, W8100 and S9150 will give you an incredible FP64 1:2 FP32 performance. For example, on a GTX 780 Ti, the FP64 performance is 1/24 FP32. Newer Hawaii architecture consumer grade GPUs are expected to provide 1:8 performance. Also, most people who are interested in using my compiler are also mostly using regular desktops and notebooks.Īs for HD graphics, I do think they can be used as nice co-processors along with the Haswell or Ivy Bridge CPU cores. So vendors like NVIDIA and AMD do not cram FP64 compute cores in their GPUs. Regular desktop processors are obviously much cheaper to buy. While I would love to have access to a Xeon Phi, I am limited by the lab's hardware budget and so don't have one. I am a PhD student working on compilers targeting multi and many-core systems. The ratio also defines FP64 performance on a GPU-by-GPU basis, so if a card has, say, 30 TFLOPS of FP32 it will have 469 GFLOPS of FP64.
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