Thank you very much for your reply.
The hardware I use is AArch64 CPU with 8 cores. I refer to this tutorial to
deploy tvm：https://tvm.apache.org/docs/deploy/cpp_deploy.html.The c++ thread
that load and use tvm library is bound to 3 intermediate frequency cpus, and
TVM_NUM_THREADS is set to 1
Thank you very much for your reply.
Ansor looks great, I am very interested, I will try it.If there is any
progress, I will be happy to discuss further with you.
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I don't think u should set `TVM_NUM_THREADS` on arm because of arm's BIG LITTLE
architecture. I think you should call `runtime.config_thread_pool` to complete
the core binding work. Another thing is we shouldn't make tvm worker thread run
different frequency cpus (aka, one worker thread is in
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Merged #6511 into master.
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Thank you very much for your reply.
As I said before, I refer to this tutorial to deploy tvm:
https://tvm.apache.org/docs/deploy/cpp_deploy.html. I export tvm.build function
as a library first, then load and call the function in C++.
According to your suggestion, I set the cpu affinity in thi
On your case, current code is will call 4 cores (id 0 ~ 3). So parallel brings
you better performance.
About time consuming functions, Do you use auto tvm? If you use auto tvm, the
default cpu TVM uses is big core (that is index 7). If you decide to use 4
little cores, you should make auto tv
Hi @mwillsey,
The decentralizing code generation sounds a good idea technically! We choose
Python mainly for user-friendly. I would also like to know @tqchen's opinion
here.
[quote="mwillsey, post:9, topic:7930"]
On the topic of checking the generated code in, I’m not sure why that is
necessa
I like the idea of using rust to generate rust side. In the meantime, a python
syntax for data structure setup can be useful in the future when we want to
design custom data types from python side. One potential solution is we keep
the python schema frontend, and generate a json exchange forma
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