Deview 2014 키노트

참석자 규모
2600 2600
1900
1800
1200
1000
2009 2010 2011 2012 2013 2014

세션수
2009
2010
2011
2012
2013
56
41
30
21
16
2014 56

외부 참여 연사
80%
71% 71%
36%
2009 2010 2011 2012 2013 2014

기술⒪컨텐츠⒪생산
기술공유

기술⒪컨텐츠⒪생산
기술공유
개발도구⒪지원

개발자⒪컨퍼런스
Ⓘ
커뮤니티
ⒽⒺ
기술학술대회
ⓀⒺ
소모임스터디
ⒾⓂⓀ
후원

개발자⒪컨퍼런스
Ⓘ
커뮤니티
ⒽⒺ
기술학술대회
ⓀⒺ
소모임스터디
ⒾⓂⓀ
후원
기술세미나
오픈세미나
ⒻⒺ
대학생세미나
Ⓕ
지방개최
Ⓘ

Ⓘ ⓐ⓿⓶⓶Ⓑ⓾⓳⓷⓯⒪개발자⒪지원
ⒽⒽ 커미터
ⓂⒼⒾ ⒼⒺⒻⒽ년⒪ⒻⒻ월⒪이후⒪커밋수
ⒼⓀⒺ만 누적⒪⓮⓹━⓸⓶⓹⓫⓮

Ⓗ ⓗ⓫⓴⓹⓼⒪⓿⓺⓮⓫⓾⓯⓽
ⒼⒽ Ⓧ⓹⓸⓾⓼⓳⓬⓿⓾⓹⓼⓽
ⓂⒺⒺ 네이버⒪내부⒪프로젝트

⓹⓺⓯⓸⒪⓽⓹⓿⓼⓭⓯⒪⒪
⓮⓳⓽⓾⓼⓳⓬⓿⓾⓯⓮⒪⓾⓼⓫⓭⓳⓸⓱⒪⒰⒪⓾⓼⓹⓿⓬⓶⓯⓽⓲⓹⓹⓾⓳⓸⓱⒪⓽┃⓽⓾⓯⓷⒪⒪
ⒼⒺⒻⒾ년⒪ⒻⒼ월⒪⓹⓺⓯⓸⒪계획

대규모⒪분산⒪시스템의⒪트랜잭션⒪흐름⒪추적

대규모⒪분산⒪시스템의⒪트랜잭션⒪흐름⒪추적
전체⒪서비스⒪서버Ⓓ인프라⒪구조와⒪상태⒪분석

ⓞ⓹⓷⓭⓫⓾⒪
Ⓥ⓺⓫⓭⓲⓯⒪ⓒⓞⓞⓚ⒪Ⓧ⓶⓳⓯⓸⓾⒪─ⒾⒸ│⒪
ⓔⓎⓕ⒪ⓒ⓾⓾⓺Ⓧ⓹⓸⓸⓯⓭⓾⓹⓼⒪
ⓗ┃ⓝⓛⓖⒶ⒪ⓍⓟⓌⓜⓓⓎⒶ⒪ⓙⓜⓋⓍⓖⓏⒶ⒪ⓗⓝⓝⓛⓖⒶ⒪ⓎⓌⓍⓚⒶ⒪⓳Ⓦ⓫⓾⓳⓽Ⓐ⒪⓷┃Ⓦ⓫⓾⓳⓽⒪
Ⓥ⓼⓭⓿⓽Ⓐ⒪ⓗ⓯⓷⓭⓫⓭⓲⓯⓮Ⓐ⒪ⓜ⓯⓮⓳⓽⒪
Ⓧ⓫⓽⓽⓫⓸⓮⓼⓫Ⓐ⒪ⓒⓌ⓫⓽⓯

기술⒪컨텐츠⒪생산⒪
기술공유⒪

기술⒪컨텐츠⒪생산⒪
기술공유⒪
스타트업지원

스타트업지원
기술력있는⒪⓯⓫⓼⓶┃⒪⓽⓾⓫⓱⓯⒪스타트업

스타트업지원
공간⒪및⒪인프라⒪지원

스타트업지원
네이버⒪개발자Ⓐ⒪디자이너⒪기술지원⒪및⒪협업

스타트업지원
필요한⒪플랫폼⒪협력⒪개발⒪Ⓓ⒪ⓙ⓺⓯⓸Ⓥⓚⓓ⒪제휴

스타트업지원
중장기⒪지원

스타트업지원
중장기⒪지원
금액⒪제한없이⒪꾸준한⒪지원Ⓓ투자

http://everystevejobsvideo.com/wp-content/uploads/2013/02/Apple-Xserve.jpg http://memcached.org/

99% disk full
/var/spool/mqueue/

99% disk full
/var/spool/mqueue/
% rm -rf /var/spool/mqueue/

http://blogs.independent.co.uk/wp-content/uploads/2013/01/twitter-fail-whale.jpg

1 [|||||||||||||||||||||||||||||||||100.0%]
2 [|| 1.2%]
3 [|| 2.0%]
4 [|| 1.5%]

100% kernel CPU usage on core 0

...

...
/proc/interrupts

ⓍⓙⓜⓏ⒪Ⓕ ⓍⓙⓜⓏ⒪Ⓖ ⓍⓙⓜⓏ⒪Ⓗ
⓮⓳⓽⓵⒪
⓭⓹⓸⓾⓼⓹⓶⓶⓯⓼
ⓘⓓⓍ
ⓓⓘⓞⓏⓜⓜⓟⓚⓞ⒪
ⓒⓋⓘⓎⓖⓏⓜ
ⓍⓙⓜⓏ⒪Ⓔ

ⓍⓙⓜⓏ⒪Ⓔ ⓍⓙⓜⓏ⒪Ⓕ ⓍⓙⓜⓏ⒪Ⓖ ⓍⓙⓜⓏ⒪Ⓗ
ⒼⒸⒼ⒪ⓑ⓲┄

━⓲┃ ⓴⓿⓽⓾ ⒼⒸⒼ ⓑ⓲┄Ⓣ

━⓲┃ ⓴⓿⓽⓾ ⒼⒸⒼ ⓑ⓲┄Ⓣ
━⓲┃ ⓸⓹⓾ ⓿⓽⓯ ⓫⓶⓶ ⓾⓲⓯ ⓭⓹⓼⓯⓽Ⓣ

ⒼⒺⒺⒾ
⓲⓾⓾⓺ⓄⒹⒹ━━━Ⓒ⓱⓫⓷⓯⓺⓭Ⓒ⓭⓹⓷Ⓓ⓳⓷⓫⓱⓯⓽Ⓓ⓶⓫⓬⓽Ⓓ⓼⓯─Ⓑ⓺ⒾⒽⓀⒷ⓬⓹│ⓖⓑⒸ⓴⓺⓱

“We’re on track, by 2010, for 30 GHz
devices, 10nm or less”
ⓚ⓫⓾⒪ⓑ⓯⓶⓽⓳⓸⓱⓯⓼⒪⒲Ⓧⓞⓙ⒪⓹⓰⒪ⓓ⓸⓾⓯⓶⒳⒪⓳⓸⒪ⒼⒺⒺⒼ

ⓓⓏⓏⓏ⒪ⒼⒺⒻⒻ⒪⒪Ⓧ⓹⓷⓺⓿⓾⓯⓼Ⓐ⒪Ⓧ⓹⓷⓺⓿⓾⓳⓸⓱⒪ⓚ⓯⓼⓰⓹⓼⓷⓫⓸⓭⓯Ⓞ⒪ⓑ⓫⓷⓯⒪ⓙ─⓯⓼⒪⓹⓼⒪ⓘ⓯│⓾⒪ⓖ⓯─⓯⓶Ⓣ

⓷⓹⓼⓯⒪⓾⓼⓫⓸⓽⓳⓽⓾⓹⓼⓽

─⓹⓶⓾⓫⓱⓯Ⓓ⓯⓸⓯⓼⓱┃⒪⒲⓺⓹━⓯⓼⒳

“전력을 줄이기 위해 CPU 속도를 줄여야 한
다. 결과적으로 하나의 칩에 코어갯수가 늘어
나야만 한다. 엑사스케일 아키텍쳐는 수십억
이상의 높은 동시성 (concurrency) 을 가진다”
ⓟⓝ⒪ⓎⓙⓏ⒪Ⓩ│⓫⓽⓭⓫⓶⓯⒪Ⓧ⓲⓫⓶⓶⓯⓸⓱⓯

ⓗ⓿⓶⓾⓳Ⓑ⓭⓹⓼⓯⒪Ⓓ⒪ⓗ⓫⓸┃Ⓑ⓭⓹⓼⓯
https://class.stanford.edu/c4x/Engineering/CS316/asset/processor.jpg

코어⒪갯수
ⒻⒺⒺⒺⒺ
ⒻⒺⒺⒺ
ⒻⒺⒺ
ⒻⒺ
Ⓖ
Ⓘ
Ⓜ
ⒽⒼ
ⒻⓃⒼ
ⒼⓂⓂⒺ

코어⒪갯수
ⒻⒺⒺⒺⒺ
ⒻⒺⒺⒺ
ⒻⒺⒺ
ⒻⒺ
Ⓖ
Ⓘ
Ⓜ
ⒽⒼ
ⒻⓃⒼ
ⒼⓂⓂⒺ
⓷⓫⓸┃Ⓑ⓭⓹⓼⓯⒪
⓽⓺⓯⓭⓳⓫⓶⒪⓺⓿⓼⓺⓹⓽⓯⒪
Ⓧ⓹⓺⓼⓹⓭⓯⓽⓽⓹⓼⓽
⓷⓿⓶⓾⓳Ⓑ⓭⓹⓼⓯⒪
⓱⓯⓸⓯⓼⓫⓶⒪⓺⓿⓼⓺⓹⓽⓯⒪
Ⓧⓚⓟ

“The Free Lunch Is Over.
A Fundamental Turn Toward Concurrency in Software”
Ⓦ┃⒪ⓒ⓯⓼⓬⒪ⓝ⓿⓾⓾⓯⓼⒪⒲ⒼⒺⒺⒿⒶ⒪ⓎⓎ⒪ⓔ⓹⓿⓼⓸⓫⓶⒳

ⓚ⓼⓹⓱⓼⓫⓷⓽⒪━⓳⓶⓶⒪⓸⓹⓾⒪⓫⓿⓾⓹⓷⓫⓾⓳⓭⓫⓶⓶┃⒪
⓼⓿⓸⒪⓰⓫⓽⓾⓯⓼⒪━⓳⓾⓲⒪⓸⓯━⒪Ⓓ⒪⓬⓯⓾⓾⓯⓼⒪Ⓧⓚⓟ⓽

Ⓘ⒪│⒪ⒼⒸⒿⓑ⓲┄⒪ⓇⓇ⒪ⒻⒺⓑ⓲┄⒪Ⓣ

http://s3v.computerhistory.org/102695713-03-03.jpg
ⓑ⓯⓸⓯⒪Ⓥ⓷⓮⓫⓲⓶

Ⓥ⓷⓮⓫⓲⓶㰙⓽⒪⓶⓫━

Ⓥ⓷⓮⓫⓲⓶㰙⓽⒪⓶⓫━
ⓝ⓯⓼⓳⓫⓶⒪⓰⓫⓭⓾⓹⓼Ⓐ

ⓝ⓯⓼⓳⓫⓶⒪⓰⓫⓭⓾⓹⓼가⒪ⒿⒺ⒯Ⓐ⒪두배⒪빠르게⒪만들수⒪있을까Ⓣ

ⓝ⓯⓼⓳⓫⓶⒪⓰⓫⓭⓾⓹⓼가⒪ⒿⒺ⒯Ⓐ⒪두배⒪빠르게⒪만들수⒪있을까Ⓣ
0 20 40 60 80 100 120
2.0
1.5
1.0
0.5
0.0
n, number of processors
Speedup
speedup =
n
1 + 0.5 Hn - 1L

그러나Ⓐ⒪
또⒪다른⒪고민㰗⒪
⓭⓹⓼⓯⒪갯수⒪한계Ⓣ

ⓎⓋⓜⓕ⒪ⓝⓓⓖⓓⓍⓙⓘ

제한된 전력때문에 칩의 모든 트랜지스터를
활용하지 못하게 된다. 새로운 혁신 없이는
“dark silicon”시대를 맞이할수 밖에 없다.
Ⓥⓜⓗ⒪Ⓧⓞⓙ⒪ⓗ⓳⓵⓯⒪ⓗ⓿⓶⓶⓯⓼Ⓐ⒪ⒼⒺⒻⒺ

Ⓘ⒪⓭⓹⓼⓯⓽⒪
ⓀⒿ⓸⓷
Ⓜ⒪⓭⓹⓼⓯⓽⒪
ⒾⒿ⓸⓷
ⒻⓀ⒪⓭⓹⓼⓯⓽⒪
ⒽⒼ⓸⓷

Ⓘ⒪⓭⓹⓼⓯⓽⒪
ⓀⒿ⓸⓷
ⓜ⓯⓫⓶⓳⓾┃
Ⓜ⒪⓭⓹⓼⓯⓽⒪
ⒾⒿ⓸⓷
⓮⓫⓼⓵⒪⓽⓳⓶⓳⓭⓹⓸
ⒻⓀ⒪⓭⓹⓼⓯⓽⒪
ⒽⒼ⓸⓷
⓼⓿⓸⓸⓳⓸⓱⒪⓫⓾⒪⓰⓿⓶⓶⒪⓰⓼⓯⓻⓿⓯⓸⓭┃

ⓚ⓹⓽⓽⓳⓬⓶⓯⒪ⓝ⓹⓶⓿⓾⓳⓹⓸⓽⒪⓰⓹⓼⒪⓸⓹━

ⓚ⓹⓽⓽⓳⓬⓶⓯⒪ⓝ⓹⓶⓿⓾⓳⓹⓸⓽⒪⓰⓹⓼⒪⓸⓹━
ⓜ⓯Ⓑ⓳⓸⓾⓼⓹⓮⓿⓭⓾⓳⓹⓸⒪⓹⓰⒪⓭⓹⓺⓼⓹⓭⓯⓽⓽⓹⓼⓽Ⓓ⓫⓭⓭⓯⓶⓯⓼⓫⓾⓹⓼⓽

ⓚ⓹⓽⓽⓳⓬⓶⓯⒪ⓝ⓹⓶⓿⓾⓳⓹⓸⓽⒪⓰⓹⓼⒪⓸⓹━
ⓜ⓯Ⓑ⓳⓸⓾⓼⓹⓮⓿⓭⓾⓳⓹⓸⒪⓹⓰⒪⓭⓹⓺⓼⓹⓭⓯⓽⓽⓹⓼⓽Ⓓ⓫⓭⓭⓯⓶⓯⓼⓫⓾⓹⓼⓽
ⓑⓚⓟⒶ⒪ⓐⓚⓑⓋ

ⓗ⓳⓭⓼⓹⓽⓹⓰⓾⒪Ⓦ⓳⓸⓱⒪⒲검색⒳⒪
ⓚ⓫⓱⓯⒪ⓜ⓫⓸⓵⓳⓸⓱⒪⓽⓯⓼─⓳⓭⓯⒪⓿⓽⓳⓸⓱⒪ⓐⓚⓑⓋ
⓲⓾⓾⓺ⓄⒹⒹ━━━Ⓒ⓯⓸⓾⓯⓼⓺⓼⓳⓽⓯⓾⓯⓭⓲Ⓒ⓭⓹⓷Ⓓ━⓺Ⓑ⓭⓹⓸⓾⓯⓸⓾Ⓓ⓿⓺⓶⓹⓫⓮⓽ⒹⒼⒺⒻⒾⒹⒺⓃⒹ⓷⓳⓭⓼⓹⓽⓹⓰⓾Ⓑ⓰⓺⓱⓫Ⓑ⓼⓫⓫⓽Ⓑ⓺⓯⓼⓰⓹⓼⓷⓫⓸⓭⓯Ⓒ⓴⓺⓱

━⓲┃⒪ⓝⓡ⒪⓯⓸⓱⓳⓸⓯⓯⓼⓽⒪⓸⓯⓯⓮⒪⓾⓹⒪⓶⓯⓫⓼⓸⒪⓲⓫⓼⓮━⓫⓼⓯Ⓣ

SOFTWARE-PRACTICE AND EXPERIENCE, VOL. 9, 219-226 (1979)
Unrolling Loops in FORTRAN*
J. J. DONGARRA AND A. R. HINDS
Argonne National Laboratory, Argonne, Illinois 60439, U.S.A.
SUhlMARY
The technique of ‘unrolling’ to improve the performance of short program loops without
resorting to assembly language coding is discussed. A comparison of the benefits of loop
‘unrolling‘ on a variety of computers using an assortment of FORTRAN compilers is
presented.
KEY WORDS Unrolled loops FORTRAN Loop efficiency Loop doubling
INTRODUCTION
It is frequently observed that the bulk of the central processor time for a program is
localized in 3 per cent of the source code.6 Often the critical code from the timing perspective
consists of one (or a few) short inner loops typified, for instance, by the scalar product of
two vectors. A simple technique for the optimization of such loops, with consequent
improvement in overall execution time, should then be most welcome. ‘Loop unrolling’ (a
generalization of ‘loop d~ubling’),a~pp lied selectively to time-consuming loops, is just
such a technique.
TECHNIQUE
When a loop is unrolled, its contents are replicated one or more times, with appropriate
adjustments to array indices and the loop increment. For instance, the DAXPYg sequence,

for (i=0; i < 500000; i++)
y[i] = y[i] + a*x[i];

for (i=0; i < 500000; i++)
// Loop unrolled
m = n - mod(n,4);
for (i=0; i<m; i+=4) {
y[i] = y[i] + a*x[i];
y[i+1] = y[i+1] + a*x[i+1];
y[i+2] = y[i+2] + a*x[i+2];
y[i+3] = y[i+3] + a*x[i+3];
}
y[i] = y[i] + a*x[i];

ⒻⒸ⒪⓳⓸⓭⓼⓯⓷⓯⓸⓾Ⓐ⒪⓾⓯⓽⓾Ⓐ⒪⓬⓼⓫⓸⓭⓲⒪⓹─⓯⓼⓲⓯⓫⓮⒪
LBB0_1:
cmpl $500, -4024(%rbp) ## TEST
jge LBB0_4
movslq -4024(%rbp), %rax
movl -4016(%rbp,%rax,4), %ecx
movslq -4024(%rbp), %rax
movl %ecx, -2016(%rbp,%rax,4)
movl -4024(%rbp), %eax
addl $1, %eax ## INCREMENT
movl %eax, -4024(%rbp)
jmp LBB0_1 ## BRANCH

ⒼⒸ⒪Ⓧⓚⓟ⒪⓳⓸⓽⓾⓼⓿⓭⓾⓳⓹⓸⒪⓺⓳⓺⓯⓶⓳⓸⓳⓸⓱⒪사용⒪⒲⓭⓹⓸⓭⓿⓼⓼⓯⓸⓭┃⒳⒪
⓺⓳⓺⓯⓶⓳⓸⓯ Ⓕ Ⓖ Ⓗ Ⓘ Ⓙ Ⓚ Ⓛ Ⓜ
Ⓕ ⓓⓐ ⓓⓎ Ⓩⓢ ⓗⓏⓗ ⓡⓌ
Ⓖ ⓓⓐ ⓓⓎ Ⓩⓢ ⓗⓏⓗ ⓡⓌ
Ⓗ ⓓⓐ ⓓⓎ Ⓩⓢ ⓗⓏⓗ ⓡⓌ
Ⓘ ⓓⓐ ⓓⓎ Ⓩⓢ ⓗⓏⓗ ⓡⓌ

ⒼⒸ⒪Ⓧⓚⓟ⒪⓳⓸⓽⓾⓼⓿⓭⓾⓳⓹⓸⒪⓺⓳⓺⓯⓶⓳⓸⓳⓸⓱⒪사용⒪⒲⓭⓹⓸⓭⓿⓼⓼⓯⓸⓭┃⒳⒪
⓺⓳⓺⓯⓶⓳⓸⓯ Ⓕ Ⓖ Ⓗ Ⓘ Ⓙ Ⓚ Ⓛ Ⓜ
Ⓕ y[ⓓⓐ i] ⓓⓎ = y[i] Ⓩⓢ + ⓗⓏⓗ a*x[i];
ⓡⓌ
Ⓖ y[ⓓⓐ i+1] = ⓓⓎ y[i+1] Ⓩⓢ + a*ⓗⓏⓗ x[i+1];
ⓡⓌ
Ⓗ y[ⓓⓐ i+2] = ⓓⓎ y[i+2] Ⓩⓢ + a*ⓗⓏⓗ x[i+2];
ⓡⓌ
Ⓘ y[ⓓⓐ i+3] = ⓓⓎ y[i+3] Ⓩⓢ + a*ⓗⓏⓗ x[i+3];
ⓡⓌ

ⒽⒸ⒪Ⓥⓖⓟ⒪특성⒪사용⒪⒲⓭⓹⓸⓭⓿⓼⓼⓯⓸⓭┃⒳⒪
독립적인⒪⓷⓿⓶⓾⓳⓺⓶⓳⓭⓫⓾⓳⓹⓸과⒪⓫⓮⓮⓳⓾⓳⓹⓸⒪⓿⓸⓳⓾⒪⓳⓸⒪Ⓥⓖⓟ⒪
⓭⓹⓸⓭⓿⓼⓼⓯⓸⓾⒪⓹⓺⓯⓼⓫⓾⓳⓹⓸⒪
y[i] = y[i] + a*x[i];
y[i+1] = y[i+1] + a*x[i+1];
y[i+2] = y[i+2] + a*x[i+2];
y[i+3] = y[i+3] + a*x[i+3];

성능고도화의⒪기본⒪
ⓟ⓸⓮⓯⓼⓽⓾⓫⓸⓮⒪Ⓓ⒪ⓟ⓾⓳⓶⓳⓽⓯⒪ⓖ⓫┃⓯⓼⓽⒪Ⓦ⓯⓶⓹━

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