Kenta Sato bicycle1885

新社会人に必須である：

勤務先との書面による「労働契約」。業務委託契約等NG。
多寡を問わず毎月払われる給料。遅配等論外である。
健康保険。
労災保険。
雇用保険。
三六協定。
年次有休。
育児休業の制度があり取得者がいる会社に勤務する。

Profiling an application in Mac OS X

Finding which process to profile

If your system is running slowly, perhaps a process is using too much CPU time and won't let other processes run smoothly. To find out which processes are taking up a lot of CPU time, you can use Apple's Activity Monitor.

The CPU pane shows how processes are affecting CPU (processor) activity:

What is the Strict Aliasing Rule and Why do we care?

(OR Type Punning, Undefined Behavior and Alignment, Oh My!)

What is strict aliasing? First we will describe what is aliasing and then we can learn what being strict about it means.

In C and C++ aliasing has to do with what expression types we are allowed to access stored values through. In both C and C++ the standard specifies which expression types are allowed to alias which types. The compiler and optimizer are allowed to assume we follow the aliasing rules strictly, hence the term strict aliasing rule. If we attempt to access a value using a type not allowed it is classified as undefined behavior(UB). Once we have undefined behavior all bets are off, the results of our program are no longer reliable.

Unfortunately with strict aliasing violations, we will often obtain the results we expect, leaving the possibility the a future version of a compiler with a new optimization will break code we th

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

Introduction

This proposal aims to define the memory model of Julia and to provide certain guarantees in the presence of data races, both by default and through providing intrinsics to allow the user to specify the level of guarantees required. This should allow native implementation in Julia of simple system primitives (like mutexes), interoperate with native system code, and aim to give generally explainable behaviors without incurring significant performance cost. Additionally, it strives to be general-purpose and yet clear about the user's intent—particularly with respect to ensuring that an atomic-type field is accessed with proper care for synchronization.

The last two points deserve particular attention, as Julia has always provided strong reflection and generic programming capabilities that has not been seen—in this synergy combination—in any other language. Therefore, we want to be careful to observe a distinction between the asymmetries of reading vs. writing that we have felt is often not given

A traditional table-based DFA implementation looks like this:

uint8_t table[NUM_STATES][256]

uint8_t run(const uint8_t *start, const uint8_t *end, uint8_t state) {
    for (const uint8_t *s = start; s != end; s++)
        state = table[state][*s];
    return state;
}

	#!/usr/bin/perl
	# Author: Todd Larason <[email protected]>
	# $XFree86: xc/programs/xterm/vttests/256colors2.pl,v 1.2 2002/03/26 01:46:43 dickey Exp $

	# use the resources for colors 0-15 - usually more-or-less a
	# reproduction of the standard ANSI colors, but possibly more
	# pleasing shades

	# colors 16-231 are a 6x6x6 color cube
	for ($red = 0; $red < 6; $red++) {

	Latency Comparison Numbers (~2012)
	----------------------------------
	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 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD