galv

/*
 * SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: Apache-2.0
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *

trxcllnt

# Apply this config conditionally to all C files
If:
  PathMatch: .*\.(c|h)$
CompileFlags:
  Compiler: /usr/bin/gcc

---

# Apply this config conditionally to all C++ files
If:

zekome

Turn off AER logging for NVMe and event severity corrected

Motherboard: Asus Pro WS WRX80E-SAGE SE WIFI
Card: Asus HYPER M.2 X16 GEN 4 CARD
NVMe: 4x Samsung SSD 980 PRO 1TB
OS: Linux fedora 5.16.12-200.fc35.x86_64

AER, advanced error reporting logs excessively:

viliampucik

# Store interactive Python shell history in ~/.cache/python_history
# instead of ~/.python_history.
#
# Create the following .config/pythonstartup.py file
# and export its path using PYTHONSTARTUP environment variable:
#
# export PYTHONSTARTUP="${XDG_CONFIG_HOME:-$HOME/.config}/pythonstartup.py"

import atexit
import os

jserv

import atexit
import ctypes
import os
import shlex
import sys
import tempfile

CMD_C_TO_SO = '{compiler} -shared -o {output} {input} {libraries}'

hikiko4ern

# /------------------------------------------\
# | don't forget to download the .tp file    |
# | and place it in the user's directory :›  |
# |                                          |
# | also install lolcat:                     |
# |  https://github.com/busyloop/lolcat      |
# \------------------------------------------/

alias test-passed='if [ "$?" -eq "0" ]; then lolcat ~/.tp -a -s 40 -d 2; fi;'

Leandros

/* Copyright (c) 2018 Arvid Gerstmann. */
/* This code is licensed under MIT license. */
#ifndef AG_RANDOM_H
#define AG_RANDOM_H

class splitmix
{
public:
    using result_type = uint32_t;
    static constexpr result_type (min)() { return 0; }

nadavrot

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

junkdog

#!/usr/bin/env bash

#     --slave /usr/bin/$1 $1 /usr/bin/$1-\${version} \\

function register_clang_version {
    local version=$1
    local priority=$2

    update-alternatives \
        --install /usr/bin/llvm-config       llvm-config      /usr/bin/llvm-config-${version} ${priority} \

t-vi

from timeit import default_timer as time
import numpy as np
from numba import cuda
import os
os.environ['NUMBAPRO_LIBDEVICE']='/usr/lib/nvidia-cuda-toolkit/libdevice/'
os.environ['NUMBAPRO_NVVM']='/usr/lib/x86_64-linux-gnu/libnvvm.so.3.1.0'

import numpy
import torch
import ctypes