flaneur2020

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flaneur2020

text = """
0.01908ms
0.02825ms
0.01729ms
0.03700ms
0.02542ms
0.02700ms
0.04529ms
0.00400ms
0.02742ms

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import subprocess
import time
from statistics import mean

# Function to measure the execution time of a command
def measure_time(command):
    start_time = time.time()
    subprocess.run(command, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
    end_time = time.time()
    return end_time - start_time

flaneur2020

When performing matrix-vector multiplication on the GPU, you can optimize the operation using parallel reduction to efficiently sum up the elements. In a Vulkan compute shader, this could be done using shared memory (local memory in GLSL) to perform a partial sum for each workgroup before completing the sum for the entire vector.

Here's a GLSL compute shader example that demonstrates a reduction pattern for summing elements. The shader assumes that the size of the matrix row (which is the size of the vector) is a power of two for simplicity. This may not be the case in a real-world scenario, so you would need to modify the shader to handle arbitrary sizes.

```glsl
#version 450

// Define the size of the workgroup
layout(local_size_x = 128) in;

flaneur2020

goroutine profile: total 60502
30061 @ 0x438cc0 0x448c43 0x835698 0x6c4c74 0x6c6ab5 0x6c8003 0x6c3a7c 0x4688d1
#	0x835697	main.sse+0x787				/data/tmp/portage/net-misc/gossipd-9999/work/src/github.intra.douban.com/push/gossipd/apps/ssed/sse.go:132
#	0x6c4c73	net/http.HandlerFunc.ServeHTTP+0x43	/usr/lib/go/src/net/http/server.go:2012
#	0x6c6ab4	net/http.(*ServeMux).ServeHTTP+0x1a4	/usr/lib/go/src/net/http/server.go:2387
#	0x6c8002	net/http.serverHandler.ServeHTTP+0xa2	/usr/lib/go/src/net/http/server.go:2807
#	0x6c3a7b	net/http.(*conn).serve+0x86b		/usr/lib/go/src/net/http/server.go:1895

30060 @ 0x438cc0 0x431b9a 0x431105 0x4d41d5 0x4d506b 0x4d504d 0x59c50f 0x5af0ae 0x608950 0x48a7b1 0x608b9c 0x607155 0x60b27b 0x60b286 0x833443 0x4688d1
#	0x431104	internal/poll.runtime_pollWait+0x54				/usr/lib/go/src/runtime/netpoll.go:203

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#!/usr/bin/env python
from __future__ import absolute_import, print_function

import os
import sys
import subprocess
import tempfile
import platform
import functools
import codecs

flaneur2020

sudo apt-get install build-essential libsqlite3-dev zlib1g-dev libncurses5-dev libgdbm-dev libbz2-dev libreadline5-dev libssl-dev libdb-dev

wget http://www.python.org/ftp/python/2.7.9/Python-2.7.9.tgz
tar -xzf Python-2.7.9.tgz
cd Python-2.7.9

./configure --prefix=/usr --enable-shared
make
sudo make install
cd ..

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var gulp = require('gulp');
var browserify = require('browserify');
var reactify = require('reactify');
var del = require('del');
var source = require('vinyl-source-stream');
var sass = require('gulp-sass');
var sourcemaps = require('gulp-sourcemaps')

var paths = {
  SCSS: ['app/css/**/*.scss'],

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  def new
    @form = flash[:new_fund_password_form] || NewFundPasswordForm.new
  end

  def create
    @form = NewFundPasswordForm.new(new_fund_password_params)
    if @form.save
      flash[:notice] = I18n.t('fund_passwords.new.success')
      redirect_to account_path
    else

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 # 线程池最小为 1，最大为 8
 threads 1, 8
  
 # 进程池大小为 3，即应用服务器的 CPU 数 + 1
 workers 3
 
 # == 线程安全性:
 #
 # - ActiveRecord 会通过连接池保证线程安全，在没有 preload_app 时，不需要重新连接。
 #