zhuangh

#include <boost/uuid/sha1.hpp>
#include <sstream>
#include <cstddef>
#include <string>

std::string Sha1sum(void *data, std::size_t count) {
  boost::uuids::detail::sha1 hasher;
  char hash[20];
  hasher.process_bytes(data, count);
  unsigned int digest[5];

zhuangh

#ifdef _MSC_VER
#include <boost/config/compiler/visualc.hpp>
#endif
#include <boost/property_tree/ptree.hpp>
#include <boost/property_tree/json_parser.hpp>
#include <boost/foreach.hpp>
#include <cassert>
#include <exception>
#include <iostream>
#include <sstream>

zhuangh

var googleapis = require('googleapis')
, OAuth2Client = googleapis.OAuth2Client
, config = require('./config.json');

var oauth2Client = new OAuth2Client(config.CLIENT_ID, config.CLIENT_SECRET, config.REDIRECT_URL);

zhuangh

github的多人协作

1. github上你可以用别人的现成的代码 直接 git clone 即可了

2. 然后你也想改代码或者贡献代码咋办？

Fork

zhuangh

Backend Architectures

Twitter

ror, scala, jetty, erlang, thrift, mongrel, comet server, my-sql, memchached, varnish, kestrel(mq), starling, gizzard, cassandra, hadoop, vertica, munin, nagios, awstats

• Scaling Twitter: Making Twitter 10000 Percent Faster
• Scaling Twitter
• Blaine Cook on Scaling Twitter - YouTube

zhuangh

Latency Comparison Numbers
--------------------------
L1 cache reference                            0.5 ns
Branch mispredict                             5   ns
L2 cache reference                            7   ns             14x L1 cache
Mutex lock/unlock                            25   ns
Main memory reference                       100   ns             20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy              3,000   ns
Send 1K bytes over 1 Gbps network        10,000   ns    0.01 ms
Read 4K randomly from SSD*              150,000   ns    0.15 ms

zhuangh

/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

zhuangh

op {
  name: "Lu"
  input_arg {
    name: "input"
    type_attr: "T"
  }
  output_arg {
    name: "l"
    description: "The lower triangular matrix L."

zhuangh

/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.

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

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,

zhuangh

import tensorflow as tf
import numpy as np
w = np.array([[3,-1,-1], [-1,5.0,-1], [1, 0 ,10]])
print(w)
W = tf.constant(w)

l, u, p, q = tf.lu(W)

pinv = tf.matrix_inverse(p)
qinv = tf.matrix_inverse(q)