+1
> -Original Message-
> From: Matti Picus
> Sent: Sunday, April 7, 2024 7:33 PM
> To: Discussion of Numerical Python
> Subject: [Numpy-discussion] Moving the weekly traige/community meetings
>
> Could we move the weekly community/triage meetings one hour later? Some
> participants
Thank you everyone. It’s been a pleasure being part of the NumPy community
Raghuveer
From: Hameer Abbasi via NumPy-Discussion
Sent: Saturday, January 27, 2024 9:20 AM
To: Discussion of Numerical Python
Cc: Hameer Abbasi
Subject: [Numpy-discussion] Re: welcome Raghuveer, Chris, Mateusz and
What processor you are running this on? np.sort uses AVX-512 accelerated
sorting for np.int32, so just wondering if you that is the reason for this
difference.
Raghuveer
> -Original Message-
> From: sal...@caltech.edu
> Sent: Wednesday, September 13, 2023 6:14 PM
> To:
I wouldn't the discount the performance impact on real world benchmarks for
these functions. Just to name a couple of examples:
* 7x speed up of np.exp and np.log results in a 2x speed up of training
neural networks like logistic regression [1]. I would expect np.tanh will show
similar
They are meant to be optimized. Any contribution to improve them further is
more than welcome.
Raghuveer
-Original Message-
From: Noah Goldstein
Sent: Thursday, November 4, 2021 10:46 AM
To: numpy-discussion@python.org
Subject: [Numpy-discussion] [RFC] - numpy/SVML appears to be
Hi Ralf,
Thank you for the acknowledgement. I am happy to contribute and hope to
continue to do so in the future.
Raghuveer
From: NumPy-Discussion
On Behalf
Of Ralf Gommers
Sent: Thursday, June 18, 2020 2:58 PM
To: Discussion of Numerical Python
Subject: [Numpy-discussion] NumPy team
nsic and
then benchmark.
Raghuveer
-Original Message-
From: NumPy-Discussion
On Behalf
Of Matti Picus
Sent: Tuesday, February 11, 2020 11:19 PM
To: numpy-discussion@python.org
Subject: Re: [Numpy-discussion] NEP 38 - Universal SIMD intrinsics
On 11/2/20 8:02 pm, Devulapalli, Rag
>> I think this doesn't quite answer the question. If I understand correctly,
>> it's about a single instruction (e.g. one needs "VEXP2PD" and it's missing
>> from the supported AVX512 instructions in master). I think the answer is
>> yes, it needs to be added for other architectures as well.
Hi everyone,
I know had raised these questions in the PR, but wanted to post them in the
mailing list as well.
1) Once NumPy adds the framework and initial set of Universal Intrinsic, if
contributors want to leverage a new architecture specific SIMD instruction,
will they be expected to
Hello,
Are Transcendental Functions SIMD vectorized in NumPy?
Raghuveer
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