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Cutlass Alternatives
Similar projects and alternatives to cutlass
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Sonar
Write Clean C++ Code. Always.. Sonar helps you commit clean C++ code every time. With over 550 unique rules to find C++ bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.
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TensorRT
PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT (by pytorch)
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mmperf
MatMul Performance Benchmarks for a Single CPU Core comparing both hand engineered and codegen kernels.
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Chess_BinaryNeuralNetwork
Training and Code Emitting Library for Binary Neural Networks
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InfluxDB
Build time-series-based applications quickly and at scale.. InfluxDB is the Time Series Platform where developers build real-time applications for analytics, IoT and cloud-native services. Easy to start, it is available in the cloud or on-premises.
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wonnx
A GPU-accelerated ONNX inference run-time written 100% in Rust, ready for the web
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flopth
A simple program to calculate and visualize the FLOPs and Parameters of Pytorch models, with handy CLI and easy-to-use Python API.
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kernel_tuner_tutorial
A hands-on introduction to tuning GPU kernels using Kernel Tuner https://github.com/KernelTuner/kernel_tuner/
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better cutlass alternative or higher similarity.
cutlass reviews and mentions
Posts with mentions or reviews of cutlass.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-04.
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How to Optimize a CUDA Matmul Kernel for CuBLAS-Like Performance: A Worklog
This is a great post for people who are new to optimizing GPU code.
It is interesting to see that the author got this far without interchanging the innermost loop over k to the outermost loop, as is done in CUTLASS (https://github.com/NVIDIA/cutlass).
As you can see in this blog post the code ends up with a lot of compile-time constants (e.g. BLOCKSIZE, BM, BN, BK, TM, TN) one way to optimize this code further is to use an auto-tuner to find the optimal value for all of these parameters for your GPU and problem size, for example Kernel Tuner (https://github.com/KernelTuner/kernel_tuner)
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pytorch example to actually see anything near 83 TFLOP/s on a RTX 4090?
Some examples here have a benchmark: https://github.com/NVIDIA/cutlass/blob/master/examples/24_gemm_grouped/gemm_grouped.cu
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[D] What are some good resources to learn CUDA programming?
If you already know some C++, the Nvidia devblog is a great resource. Going further, Cub and Cutlass provide examples of efficient implementations for key operations at all hardware levels. Finally, this is more anecdotal but I always start my lectures on Cuda programming with the pictures in this doc page, to provide some intuition on the different memory layers that you can leverage to speed up a program. In any case, good luck :-)
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PyTorch on Apple M1 Faster Than TensorFlow-Metal
So with Tensorcores you use TF32 which is more like FP19-ish and the marketing makes you think you get 8x the performance. But if you want actual FP32 precision you will need something like [1] but then your performance in the Tensorcore path is _only_ 2X faster than the SIMT path.
I'll leave the prefix sum for other devs who know more :D
https://github.com/NVIDIA/cutlass/blob/master/examples/27_am...
//part of nod.ai/shark team
- 100% Accurate Binary Neuronal Networks
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A note from our sponsor - Sonar
www.sonarsource.com | 7 Feb 2023
Stats
Basic cutlass repo stats
11
2,461
8.4
7 days ago
NVIDIA/cutlass is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
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