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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Benchmarking Deep Learning operations on different hardware
| Date | Stars |
|---|---|
| 2026-07-31 | 1105 |
| 2026-08-06 | 1106 |
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 - [DeepBench](#deepbench) - [Types of Operations](#types-of-operations) - [Training Benchmark](#training-benchmark) - [Inference Benchmark](#inference-benchmark) - [Supported Ops & Precision](#supported-ops-and-precision) - [Results](#results) - [Get Involved](#get-involved) - [Getting the Code](#getting-the-code) # DeepBench The primary purpose of DeepBench is to benchmark operations that are important to deep learning on different hardware platforms. Although the fundamental computations behind deep learning are well understood, the way they are used in practice can be surprisingly diverse. For example, a matrix multiplication may be compute-bound, bandwidth-bound, or occupancy-bound, based on the size of the matrices being multiplied and the kernel implementation. Because every deep learning model uses these operations with different parameters, the optimization space for hardware and software targeting deep learning is large and underspecified. DeepBench attempts to answer the question, "Which hardware provides the best performance on the basic operations used for deep neural networks?". We specify these operations at a low level, suitable for use in hardware simulators for groups building new processors targeted at deep learning. DeepBench includes operations and workloads that are important to both training and inference. ## Where does DeepBench fit in? The Deep Learning eco system consists of several different pieces. We wanted to highlight where DeepBench fits into this eco system. The diagram below describes the software and hardware components involved with deep learning. At the very top, deep learning frameworks like Baidu's [PaddlePaddle](https://github.com/baidu/Paddle), Theano, TensorFlow, Torch etc. All these frameworks allow deep learning researchers to build models. They include basic building blocks like layers which can be connected in different ways to create a model. In order to train the deep learning models, the frameworks work with underlying neural network libraries such as NVIDIA's cuDNN and Intel's MKL. These libraries implement operations such as matrix multiply that are important to deep learning models. Finally, the models are trained on hardware like NVIDIA GPUs or Intel's Xeon Phi processor. <img src="doc/deepbench.png" height=300> DeepBench uses the neural network libraries to benchmark the performance of basic operations on different hardware. It does not work with deep learning frameworks or deep learning models built for applications. We cannot measure the time required to train an entire model using DeepBench. The performance characteristics of models built for different applications are very different from each other. Therefore, we are benchmarking the underlying operations involved in a deep learning model. Benchmarking these operations will help raise awareness amongst hardware vendors and software developers about the bottlenecks in deep learning training and inference. ## Methodology DeepBench consists of a set of basic operations (dense matrix multiplies, convolutions and communication) as well as some recurrent layer types. There are Excel spreadsheets (`DeepBenchKernels_train.xlsx` & `DeepBenchKernels_inference.xlsx`) in this repository that describes all of the sizes for training and inference respectively. For training, both forward and backward operations are tested. The precision requirements for training and inference are discussed in the sections below. We will use vendor supplied libraries even if faster independent libraries exist or faster results have been published. Most users will default to the vendor supplied libraries and as such the vendor supplied libraries are most representative of users' experience. ## Entry DeepBench includes training results for seven hardware platforms, NVIDIA's TitanX, M40, TitanX Pascal, TitanXp, 1080 Ti, P100 and Intel's Knights Landing. Inference results are i
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matched fp:b8c6bf13488bc339, llm:Repository description: "Benchmarking Deep Learning operations on different hardware"; language C++; project known as DeepBench by Baidu for benchmarking DL ops (kernels) across hardware.
matched fp:b8c6bf13488bc339, llm:Repository description: "Benchmarking Deep Learning operations on different hardware"; language C++; project known as DeepBench by Baidu for benchmarking DL ops (kernels) across hardware.
matched fp:b8c6bf13488bc339, llm:Repository description: "Benchmarking Deep Learning operations on different hardware"; language C++; project known as DeepBench by Baidu for benchmarking DL ops (kernels) across hardware.