daskol

#!/bin/bash
#   dev-null.sh
#   This shell script implement /dev/null as a Service(//aaS).

IFS=$'\n\n'

while true; do
    rm -rf /tmp/input /tmp/output
    mkfifo /tmp/input /tmp/output

daskol

--- weak-ordering.asm  2018-03-15 14:59:41.504213243 +0300
+++ stong-ordering.asm 2018-03-15 14:59:32.910123855 +0300
@@ -9,7 +9,7 @@
        sub    $0x38,%rsp
        mov    0x78(%rdi),%eax
        test   %eax,%eax
+       jne    0x40a20a <StressPushPop(benchmark::State&)+490>
-       jne    0x40a218 <StressPushPop(benchmark::State&)+504>
        mov    $0x409700,%r13d
        mov    $0x62ff68,%r15d

daskol

from typing import List, Tuple
from random import randint


def gen_array(random=True, size=8 * 1024 * 1024):
    if random:
        return [randint(-1000, 1000) for i in range(size)]
    else:
        return list(sorted(gen_array(True, size)))

daskol

/**
 *  \file endianness.h
 *  \brief This header defines common routine for convertion from network
 *  ordered integers(which is usually big endian) to byte order that is native
 *  to host.
 *
 *  There is if-constexpr so C++17 compiler is strongly needed for this.
 */

#pragma once

daskol

#   FindFontConfig.cmake
#   Daniel Bershatsky, 2018

#.rst:
#   FindFontConfig
#   --------------
#
# Find FontConfig library for configuring and customizing font access.
#
# Imported Targets

daskol

//  socket-binding.c
//
//  This is small tool that helps to clarify how to listen IPv6 socket and it
//  is nessesary or not listen IPv4 socket at the same time.
//
//  Compile `gcc -std=c11 -o listen main.c`
//
//  Run binary with option `-4` or `-6` and connect to port(default is 5489)
//  with netcat in the following way.
//      1. `nc -4 127.0.0.1 5489`

daskol

/**
 * \file tty-size.c
 * \brief Get size of console attached to stderr.
 */
#include <stdio.h>
#include <string.h>

#include <errno.h>
#include <sys/ioctl.h>
#include <unistd.h>

daskol

import torch as T

class Maxout(T.nn.Module):
    """Class Maxout implements maxout unit introduced in paper by Goodfellow et al, 2013.
    
    :param in_feature: Size of each input sample.
    :param out_feature: Size of each output sample.
    :param n_channels: The number of linear pieces used to make each maxout unit.
    :param bias: If set to False, the layer will not learn an additive bias.
    """

daskol

#   encoding: utf8
#   filename: maxout.py

import torch as T
import torch.nn.init as I

class TTMaxout(T.nn.Module):

    def __init__(self, in_features, out_features, n_channels, tt_rank,
                 bias=True, irange=0.005):

daskol

#   encoding: utf8
#   filename: optimizer.py

import torch as T


def clip_momentum(value: float) -> float:
    if value > 1.0:
        return 1.0
    elif value < 0.0: