Su also introduced the Alveo V70 AI inference accelerator. The Instinct isn't the only enterprise announcement at CES. Su said the chip is in the labs now and sampling to select customers, with a launch expected in the second half of the year. Mind you, AMD's MI250 is an impressive piece of silicon, used in the first exascale supercomputer, Frontier, at the Oak Ridge National Lab.ĪMD's MI300 chip is similar to what Intel is doing with Falcon Shores, due in 2024, and Nvidia is doing with its Grace Hopper Superchip, due later this year. She mentioned the much-hyped AI chatbot ChatGPT and noted it takes months to train the models the MI300 will cut the training time from months to weeks, which could save millions of dollars on electricity, Su said. Su said the MI300 delivers eight times the AI performance and five times the performance per watt of the Instinct MI250. What this allows us to do is share system resources for the memory and IO, and it results in a significant increase in performance and efficiency as well as much easier to program.” ![]() It also allows the CPU and GPU to work on the same data in memory simultaneously, which speeds up processing.ĪMD CEO Lisa Su announced the chip at the end of her 90-minute CES keynote, saying MI300 is “the first chip that brings together a CPU, GPU, and memory into a single integrated design. Data doesn’t need to go from the CPU or GPU to DRAM it goes out to the HBM stack, drastically reducing latency. The 3D design allows for tremendous data throughput between the CPU, GPU and memory dies. Rounding off the Instinct MI300 is 128MB of HBM3 memory stacked in a 3D design. AMD has not said how many GPU cores per chiplet there are. CDNA is the data center version of AMD's RDNA consumer graphics technology. Benchmarks often neglect this transfer overhead.The Instinct MI300 has 24 Zen 4 CPU cores and six CDNA chiplets. Also, moving data from main memory to GPU memory is a very expensive operation because of the relatively low memory bandwidth provided by PCI-Express. A comparison of hardware specifications shows that GPUs may offer up to ten-fold performance gains, and higher gains are only possible and very specialized scenarios. The library has gained a lot of additional functionality since then and now supports both dense and sparse linear algebra using CUDA, OpenCL, and OpenMP compute backends.įinally, it is important to keep the following in mind: Even though some publications on GPUs claim speed-ups of a factor or hundred or more over a traditional CPU-based implementation, this is not backed up by actual hardware and thus only shows that the reference implementation is bad. For the case of linear algebra operations, Florian Rudolf and I put a high-level C++ interface on top of various compute kernels for iterative solvers and released our results as ViennaCL. ![]() Programming environments for GPUs, however, only provide low-level access to the hardware. For certain parallel algorithms it may even pay off to run general purpose computations on graphics processing units (GPUs), which are by nature tailored to efficiently work in parallel. Instead, multiple cores can now be found on modern CPUs and hence it is required to employ suitable parallel algorithms in order to make use of the higher performance now provided via additional cores rather than higher clock frequency. ![]() While central processing units (CPUs) doubled their speed approximately every two years up until the early 2000s, physical limitations no longer allow such improvements for a single core.
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