crowwork

Engineering and design contain tradeoffs, and one is better than other is quite subjective and must put into the context.

We would certainly welcome healthy discussions, where I would be more than happy to talk about the different design choices we made and why some only makes sense for one project but not another due to different goals. That would better happen at in issues, forums though, but not an hn thread

please stop doing this, it is sad to see fires being set up on threads which belong to Halide. TVM benefit a lot from its Halide ancestry, and we are deeply grateful for that.

I personally made mistake initially, which I deeply regretted, for not adding a clear citation to Halide in some of our codebase where we reused useful hacks introduced in Halide codebase. We fixed that issue a year ago.

The two projects now have very different design priorities and focus. We as a community should not hijack the thread that belongs to Halide.

Tianqi - TVM PMC

indeed, they refer to GPU (kernel) programs

TVM comitter here, we have benefited a lot from the Halide community in particular its IR, and we are very grateful of that. I personally think it is wrong to plug-in the ads here as this post is about Halide.

There is no good or bad choices of IR, and both Halide and TVM make reasonable technical decisions to fit their use cases

Halide’s new differentiable programing support is a great that has not yet been supported by TVM. we anticipate it would have awesome usecases

The Versatile Tensor Accelerator (VTA) is an extension of the TVM framework designed to advance deep learning and hardware innovation.

- docs https://docs.tvm.ai/vta/ - techreport https://arxiv.org/abs/1807.04188

the benchmarks are not about GEMM, but real-world deep learning workloads which could have very different characteristics from GEMM

great discussion about the difference between mobile and normal gpu

part of TVM https://github.com/dmlc/tvm is built with primitives in Halide. Halide is indeed super cool and TVM benefit a lot from its experience. While Halide optimizes CPU and image processing workload well. TVM also focus specifically on deep learning and offers more optimizations on multi-core, GPU and other hardwares. This requires rethink of quite a lot of designs.

NNVM compiler is built on top of TVM, with additional graph level optimizers, and the two forms an end to end pipeline

as far as i know weld is optimized for data analytics workload so far(dataframe) while tvm is optimized for tensor deep learning workloads with great cpu, gpu and other supports

it means you can declare gpu array like those in numpy/torch, write them imperatively from python side, and mix them with the graph computation, instead of forcing everything to be part of a graph