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Cutlass Alternatives
Similar projects and alternatives to cutlass
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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wonnx
A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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mmperf
MatMul Performance Benchmarks for a Single CPU Core comparing both hand engineered and codegen kernels.
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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 2023-03-23.
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Optimization Techniques for GPU Programming [pdf]
I would recommend the course from Oxford (https://people.maths.ox.ac.uk/gilesm/cuda/). Also explore the tutorial section of cutlass (https://github.com/NVIDIA/cutlass/blob/main/media/docs/cute/...) if you want to learn more about high performance gemm.
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Want to understand INT8 better
The latter (and I guess you were asking about this one) is designed to accelerate NN inference in reduced precision. It is possible to use Tensor Cores for you own purposes, mainly through CUTLASS. But because Tensor Cores are designed to execute matrix multiplications, it can be hard to adapt your problem to them. The performance with them is insane (IIRC 32x the performance of the INT32 pipeline), but only for matrix multiplication…
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How do I deal with tensor core and cuda core with different precision?
If you want to learn about controlling Tensor Cores, the main way is through the CUTLASS library, that wraps the complexity of Tensor Cores into higher level abstractions. You can also look for mma/wmma instructions in the PTX specification, or for the WMMA API in CUDA.
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AI’s compute fragmentation: what matrix multiplication teaches us
> we used tensor cores and managed to get back fp32 accuracy with 3 rounds of the things
Hey are you referring to 3xTF32 (https://github.com/NVIDIA/cutlass/tree/master/examples/28_am...)? IMO this is a perfect example where proper abstraction could save engineers non-trivial amount of time - imagine a compiler stack which allows 3xTF32 as a normal dtype and subsequent analysis compatible with this special dtype :-)
- With LLVM and MLIR, is manual cuda optimizing still important?
- CUTLASS 3.0 is now available
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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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Create a bare CMake for Nvidia CUTLASS
I would like to make a minimum CMakeLists to use the CUDA CUTLASS library in another project. The build system is CMake, however I have little experience with CMake.
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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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A note from our sponsor - SaaSHub
www.saashub.com | 25 Apr 2024
Stats
Basic cutlass repo stats
16
4,522
8.8
3 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.
The primary programming language of cutlass is C++.
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