![]() But Nvidia maintains that the H100 can “intelligently” handle scaling for each model and offer up to triple the floating point operations per second compared with prior-generation TF32, FP64, FP16 and INT8 precisions. Typically, lower precisions, like FP8, translate to less accurate models. The challenge in training AI models is to maintain accuracy while capitalizing on the performance offered by smaller, faster formats like FP8. Cleverly, Transformer Engine uses Nvidia’s fourth-generation tensor cores to apply mixed FP8 and FP16 formats, automatically choosing between FP8 and FP16 calculations based on “custom, -tuned” heuristics, according to Nvidia. Most AI floating-point math is done using 16-bit half precision (FP16), 32-bit single precision (FP32), and 64-bit double precision (FP64). AI training relies on floating-point numbers, which have fractional components (e.g., 3.14). The H100’s Transformer Engine leverages what’s called 16-bit floating-point precision and a newly added 8-bit floating-point data format. OpenAI’s language-generating GPT-3 and DeepMind’s protein shape-predicting AlphaFold are built atop Transformer, and research has shown that the Transformer can be trained to play games like chess and even generate images. Transformers have been widely deployed in the real world. Dating back to 2017, the Transformer has become the architecture of choice for natural language models (i.e., AI models that process text), thanks in part to its aptitude for summarizing documents and translating between languages. Transformer Engineīeyond DPX, Nvidia is spotlighting the H100’s Transformer Engine, which combines data formats and algorithms to speed up the hardware’s performance with Transformers. But Nvidia claims that the DPX instructions on the H100 can accelerate dynamic programming by up to seven times compared with Ampere-based GPUs. ![]() These algorithms typically run on CPUs or specially designed chips called field-programmable gate arrays (FPGAs). In memorization, the answers to these sub-problems are stored so that the sub-problems don’t need to be recomputed when they’re needed later on in the main problem.ĭynamic programming is used to find optimal routes for moving machines (e.g., robots), streamline operations on sets of databases, align unique DNA sequences, and more. Recursion in dynamic programming involves breaking a problem down into sub-problems, ideally saving time and computational effort. Developed in the 1950s, dynamic programming is an approach to solving problems using two key techniques: recursion and memoization. The H100 is the first Nvidia GPU to feature dynamic programming instructions (DPX), “instructions” in this context referring to segments of code containing steps that need to be executed. MetaBeat will bring together thought leaders to give guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA.
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