elbencho VS oneflow

You can also use elbencho (https://github.com/breuner/elbencho) which is functionally equivalent to IOR but a little more flexible.

I won't speak for him but Tallis is definitely aware (see his recent article on updated testing) and so are others. I regularly work with Sean Webster of Tom's Hardware (/u/TurboSSD) and we spend an insane amount of time working around SLC cache response and discussing it on my discord. These guys often use different tools (e.g. Iometer vs. FIO) although the one I've been playing with moving forward is elbencho. Either way, it's something that takes up a lot of time in SSD reviewer circles since it's a relatively tightknit group. It's a challenging topic especially as SLC caching algorithms are getting more complex, with behavioral and performance-based profiles and reviewers already doing preconditioning.

We are thrilled to announce the new release of OneFlow,, which is a deep learning framework designed to be user-friendly, scalable and efficient. OneFlow v0.9.0 contains 640 commits. For the full changelog, please check out: https://github.com/Oneflow-Inc/oneflow/releases/tag/v0.9.0.

Found relevant code at https://github.com/Oneflow-Inc/oneflow + all code implementations here

Check out OneFlow on GitHub . We'd love to hear your feedback!

OneFlow URL：https://github.com/Oneflow-Inc/oneflow/

No other options?:) We are developing a new distributed DL framework called OneFlow, which is faster than other frameworks and easier to use. Now it provides more and better PyTorch compatible APIs.

Code for https://arxiv.org/abs/2110.15032 found: https://github.com/Oneflow-Inc/oneflow

This article focuses on what is happening behind the execution of a Tensor in the deep learning framework OneFlow. It takes the operator oneflow.relu as an example to introduce the Interpreter and VM mechanisms that need to be relied on to execute this operator.

This article describes how OneFlow works with MLIR, how to add a graph-level Pass to OneFlow IR, how OneFlow Operations automatically become MLIR Operations, and why OneFlow IR can use MLIR to accelerate computations.

Backpressure mechanism, also known as credit-based flow control, is a classic scheme for network communication flow control problems. Its predecessor is the TCP sliding window. This idea is particularly simple and effective. As we will see in this article, based on the same principles, this idea is applicable to any flow control scheme and is found in the design of many hardware and software systems. In this article, the engineer of OneFlow will tell the chequered history of this simple idea.