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High-performance computing (HPC) as a term arose after the term "supercomputing". [3] HPC is sometimes used as a synonym for supercomputing; but, in other contexts, "supercomputer" is used to refer to a more powerful subset of "high-performance computers", and the term "supercomputing" becomes a subset of "high-performance computing".
The LINPACK performance gives some indication of performance for some real-world problems, but does not necessarily match the processing requirements of many other supercomputer workloads, which for example may require more memory bandwidth, or may require better integer computing performance, or may need a high performance I/O system to ...
A GPU cluster is a computer cluster in which each node is equipped with a graphics processing unit (GPU). By harnessing the computational power of modern GPUs via general-purpose computing on graphics processing units (GPGPU), very fast calculations can be performed with a GPU cluster.
The Google Tensor SoC demonstrated "extremely large performance advantages over the competition" in machine learning-focused benchmarks; although instantaneous power consumption also was relatively high, the improved performance meant less energy was consumed due to shorter periods requiring peak performance.
Sycamore is a transmon superconducting quantum processor created by Google's Artificial Intelligence division. [1] It has 53 qubits. [2] In 2019, Sycamore completed a task in 200 seconds that Google claimed, in a Nature paper, would take a state-of-the-art supercomputer 10,000 years to finish. Thus, Google claimed to have achieved quantum ...
Google Compute Engine Unit (GCEU), which is pronounced as GQ, is an abstraction of computing resources. According to Google, 2.75 GCEUs represent the minimum power of one logical core (a hardware hyper-thread) based on the Sandy Bridge platform. The GCEU was created by Anthony F. Voellm out of a need to compare the performance of virtual ...
2×10 15: Nvidia DGX-2 a 2 Petaflop Machine Learning system (the newer DGX A100 has 5 Petaflop performance) 11.5×10 15: Google TPU pod containing 64 second-generation TPUs, May 2017 [9] 17.17×10 15: IBM Sequoia's LINPACK performance, June 2013 [10] 20×10 15: roughly the hardware-equivalent of the human brain according to Ray Kurzweil.
A Google Tensor Processing Unit v4 pod is capable of 1.1 exaflops of peak performance, [98] while TPU v5p claims over 4 exaflops in Bfloat16 floating-point format, [99] however these units are highly specialized to run machine learning workloads and the TOP500 measures a specific benchmark algorithm using a specific numeric precision.