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- 5. 참석자 규모
- 6. 참석자 규모 2600 2600 1900 1800 1200 1000 2009 2010 2011 2012 2013 2014
- 7. 세션수
- 8. 세션수 2009 2010 2011 2012 2013 56 41 30 21 16 2014 56
- 9. 외부 참여 연사
- 10. 외부 참여 연사 80% 71% 71% 36% 2009 2010 2011 2012 2013 2014
- 14. 기술⒪컨텐츠⒪생산
- 15. 기술⒪컨텐츠⒪생산 기술공유
- 17. 후원
- 18. 개발자⒪컨퍼런스 Ⓘ 커뮤니티 ⒽⒺ 기술학술대회 ⓀⒺ 소모임스터디 ⒾⓂⓀ 후원
- 19. 개발자⒪컨퍼런스 Ⓘ 커뮤니티 ⒽⒺ 기술학술대회 ⓀⒺ 소모임스터디 ⒾⓂⓀ 후원 기술세미나 오픈세미나 ⒻⒺ 대학생세미나 Ⓕ 지방개최 Ⓘ
- 23. Ⓘ ⓐ⓿⓶⓶Ⓑ⓾⓳⓷⓯⒪개발자⒪지원 ⒽⒽ 커미터 ⓂⒼⒾ ⒼⒺⒻⒽ년⒪ⒻⒻ월⒪이후⒪커밋수 ⒼⓀⒺ만 누적⒪⓮⓹━⓸⓶⓹⓫⓮
- 25. Ⓗ ⓗ⓫⓴⓹⓼⒪⓿⓺⓮⓫⓾⓯⓽ ⒼⒽ Ⓧ⓹⓸⓾⓼⓳⓬⓿⓾⓹⓼⓽ ⓂⒺⒺ 네이버⒪내부⒪프로젝트
- 33. ⓞ⓹⓷⓭⓫⓾⒪ Ⓥ⓺⓫⓭⓲⓯⒪ⓒⓞⓞⓚ⒪Ⓧ⓶⓳⓯⓸⓾⒪─ⒾⒸ│⒪ ⓔⓎⓕ⒪ⓒ⓾⓾⓺Ⓧ⓹⓸⓸⓯⓭⓾⓹⓼⒪ ⓗ┃ⓝⓛⓖⒶ⒪ⓍⓟⓌⓜⓓⓎⒶ⒪ⓙⓜⓋⓍⓖⓏⒶ⒪ⓗⓝⓝⓛⓖⒶ⒪ⓎⓌⓍⓚⒶ⒪⓳Ⓦ⓫⓾⓳⓽Ⓐ⒪⓷┃Ⓦ⓫⓾⓳⓽⒪ Ⓥ⓼⓭⓿⓽Ⓐ⒪ⓗ⓯⓷⓭⓫⓭⓲⓯⓮Ⓐ⒪ⓜ⓯⓮⓳⓽⒪ Ⓧ⓫⓽⓽⓫⓸⓮⓼⓫Ⓐ⒪ⓒⓌ⓫⓽⓯
- 35. 기술⒪컨텐츠⒪생산⒪ 기술공유⒪ 개발도구⒪지원 스타트업지원
- 36. 스타트업지원
- 39. 스타트업지원 기술력있는⒪⓯⓫⓼⓶┃⒪⓽⓾⓫⓱⓯⒪스타트업 공간⒪및⒪인프라⒪지원 네이버⒪개발자Ⓐ⒪디자이너⒪기술지원⒪및⒪협업
- 40. 스타트업지원 기술력있는⒪⓯⓫⓼⓶┃⒪⓽⓾⓫⓱⓯⒪스타트업 공간⒪및⒪인프라⒪지원 네이버⒪개발자Ⓐ⒪디자이너⒪기술지원⒪및⒪협업 필요한⒪플랫폼⒪협력⒪개발⒪Ⓓ⒪ⓙ⓺⓯⓸Ⓥⓚⓓ⒪제휴
- 41. 스타트업지원 기술력있는⒪⓯⓫⓼⓶┃⒪⓽⓾⓫⓱⓯⒪스타트업 공간⒪및⒪인프라⒪지원 네이버⒪개발자Ⓐ⒪디자이너⒪기술지원⒪및⒪협업 필요한⒪플랫폼⒪협력⒪개발⒪Ⓓ⒪ⓙ⓺⓯⓸Ⓥⓚⓓ⒪제휴 중장기⒪지원
- 42. 스타트업지원 기술력있는⒪⓯⓫⓼⓶┃⒪⓽⓾⓫⓱⓯⒪스타트업 공간⒪및⒪인프라⒪지원 네이버⒪개발자Ⓐ⒪디자이너⒪기술지원⒪및⒪협업 필요한⒪플랫폼⒪협력⒪개발⒪Ⓓ⒪ⓙ⓺⓯⓸Ⓥⓚⓓ⒪제휴 중장기⒪지원 금액⒪제한없이⒪꾸준한⒪지원Ⓓ투자
- 43. http://everystevejobsvideo.com/wp-content/uploads/2013/02/Apple-Xserve.jpg http://memcached.org/
- 44. http://everystevejobsvideo.com/wp-content/uploads/2013/02/Apple-Xserve.jpg http://memcached.org/
- 45. http://everystevejobsvideo.com/wp-content/uploads/2013/02/Apple-Xserve.jpg http://memcached.org/
- 46. http://everystevejobsvideo.com/wp-content/uploads/2013/02/Apple-Xserve.jpg http://memcached.org/
- 47. 99% disk full
- 48. 99% disk full /var/spool/mqueue/
- 49. 99% disk full /var/spool/mqueue/ % rm -rf /var/spool/mqueue/
- 52. % rm -rf /var/spool/mqueue/^C
- 53. 1 [|||||||||||||||||||||||||||||||||100.0%] 2 [|| 1.2%] 3 [|| 2.0%] 4 [|| 1.5%]
- 54. 100% kernel CPU usage on core 0
- 55. 100% kernel CPU usage on core 0 ...
- 56. 100% kernel CPU usage on core 0 ... /proc/interrupts
- 57. ⓍⓙⓜⓏ⒪Ⓕ ⓍⓙⓜⓏ⒪Ⓖ ⓍⓙⓜⓏ⒪Ⓗ ⓮⓳⓽⓵⒪ ⓭⓹⓸⓾⓼⓹⓶⓶⓯⓼ ⓘⓓⓍ ⓓⓘⓞⓏⓜⓜⓟⓚⓞ⒪ ⓒⓋⓘⓎⓖⓏⓜ ⓍⓙⓜⓏ⒪Ⓔ
- 58. ⓍⓙⓜⓏ⒪Ⓕ ⓍⓙⓜⓏ⒪Ⓖ ⓍⓙⓜⓏ⒪Ⓗ ⓮⓳⓽⓵⒪ ⓭⓹⓸⓾⓼⓹⓶⓶⓯⓼ ⓘⓓⓍ ⓓⓘⓞⓏⓜⓜⓟⓚⓞ⒪ ⓒⓋⓘⓎⓖⓏⓜ ⓍⓙⓜⓏ⒪Ⓔ
- 59. ⓍⓙⓜⓏ⒪Ⓔ ⓍⓙⓜⓏ⒪Ⓕ ⓍⓙⓜⓏ⒪Ⓖ ⓍⓙⓜⓏ⒪Ⓗ ⒼⒸⒼ⒪ⓑ⓲┄
- 60. ⓍⓙⓜⓏ⒪Ⓔ ⓍⓙⓜⓏ⒪Ⓕ ⓍⓙⓜⓏ⒪Ⓖ ⓍⓙⓜⓏ⒪Ⓗ ⒼⒸⒼ⒪ⓑ⓲┄ ━⓲┃ ⓴⓿⓽⓾ ⒼⒸⒼ ⓑ⓲┄Ⓣ
- 61. ⓍⓙⓜⓏ⒪Ⓔ ⓍⓙⓜⓏ⒪Ⓕ ⓍⓙⓜⓏ⒪Ⓖ ⓍⓙⓜⓏ⒪Ⓗ ⒼⒸⒼ⒪ⓑ⓲┄ ━⓲┃ ⓴⓿⓽⓾ ⒼⒸⒼ ⓑ⓲┄Ⓣ ━⓲┃ ⓸⓹⓾ ⓿⓽⓯ ⓫⓶⓶ ⓾⓲⓯ ⓭⓹⓼⓯⓽Ⓣ
- 64. “We’re on track, by 2010, for 30 GHz devices, 10nm or less” ⓚ⓫⓾⒪ⓑ⓯⓶⓽⓳⓸⓱⓯⓼⒪⒲Ⓧⓞⓙ⒪⓹⓰⒪ⓓ⓸⓾⓯⓶⒳⒪⓳⓸⒪ⒼⒺⒺⒼ
- 68. ⓷⓹⓼⓯⒪⓾⓼⓫⓸⓽⓳⓽⓾⓹⓼⓽
- 70. ⓲⓯⓫⓾
- 71. “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
- 74. 코어⒪갯수 ⒻⒺⒺⒺⒺ ⒻⒺⒺⒺ ⒻⒺⒺ ⒻⒺ Ⓖ Ⓘ Ⓜ ⒽⒼ ⒻⓃⒼ ⒼⓂⓂⒺ
- 75. 코어⒪갯수 ⒻⒺⒺⒺⒺ ⒻⒺⒺⒺ ⒻⒺⒺ ⒻⒺ Ⓖ Ⓘ Ⓜ ⒽⒼ ⒻⓃⒼ ⒼⓂⓂⒺ ⓷⓫⓸┃Ⓑ⓭⓹⓼⓯⒪ ⓽⓺⓯⓭⓳⓫⓶⒪⓺⓿⓼⓺⓹⓽⓯⒪ Ⓧ⓹⓺⓼⓹⓭⓯⓽⓽⓹⓼⓽ ⓷⓿⓶⓾⓳Ⓑ⓭⓹⓼⓯⒪ ⓱⓯⓸⓯⓼⓫⓶⒪⓺⓿⓼⓺⓹⓽⓯⒪ Ⓧⓚⓟ
- 76. “The Free Lunch Is Over. A Fundamental Turn Toward Concurrency in Software” Ⓦ┃⒪ⓒ⓯⓼⓬⒪ⓝ⓿⓾⓾⓯⓼⒪⒲ⒼⒺⒺⒿⒶ⒪ⓎⓎ⒪ⓔ⓹⓿⓼⓸⓫⓶⒳
- 83. Ⓥ⓷⓮⓫⓲⓶㰙⓽⒪⓶⓫━
- 88. ⓝ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
- 91. ⓎⓋⓜⓕ⒪ⓝⓓⓖⓓⓍⓙⓘ
- 92. 제한된 전력때문에 칩의 모든 트랜지스터를 활용하지 못하게 된다. 새로운 혁신 없이는 “dark silicon”시대를 맞이할수 밖에 없다. Ⓥⓜⓗ⒪Ⓧⓞⓙ⒪ⓗ⓳⓵⓯⒪ⓗ⓿⓶⓶⓯⓼Ⓐ⒪ⒼⒺⒻⒺ
- 93. Ⓘ⒪⓭⓹⓼⓯⓽⒪ ⓀⒿ⓸⓷ Ⓜ⒪⓭⓹⓼⓯⓽⒪ ⒾⒿ⓸⓷ ⒻⓀ⒪⓭⓹⓼⓯⓽⒪ ⒽⒼ⓸⓷
- 94. Ⓘ⒪⓭⓹⓼⓯⓽⒪ ⓀⒿ⓸⓷ ⓜ⓯⓫⓶⓳⓾┃ Ⓜ⒪⓭⓹⓼⓯⓽⒪ ⒾⒿ⓸⓷ ⓮⓫⓼⓵⒪⓽⓳⓶⓳⓭⓹⓸ ⒻⓀ⒪⓭⓹⓼⓯⓽⒪ ⒽⒼ⓸⓷ ⓼⓿⓸⓸⓳⓸⓱⒪⓫⓾⒪⓰⓿⓶⓶⒪⓰⓼⓯⓻⓿⓯⓸⓭┃
- 102. 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,
- 103. for (i=0; i < 500000; i++) y[i] = y[i] + a*x[i];
- 104. 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];
- 105. ⒻⒸ⒪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
- 106. ⒼⒸ⒪Ⓧⓚⓟ⒪⓳⓸⓽⓾⓼⓿⓭⓾⓳⓹⓸⒪⓺⓳⓺⓯⓶⓳⓸⓳⓸⓱⒪사용⒪⒲⓭⓹⓸⓭⓿⓼⓼⓯⓸⓭┃⒳⒪ ⓺⓳⓺⓯⓶⓳⓸⓯ Ⓕ Ⓖ Ⓗ Ⓘ Ⓙ Ⓚ Ⓛ Ⓜ Ⓕ ⓓⓐ ⓓⓎ Ⓩⓢ ⓗⓏⓗ ⓡⓌ Ⓖ ⓓⓐ ⓓⓎ Ⓩⓢ ⓗⓏⓗ ⓡⓌ Ⓗ ⓓⓐ ⓓⓎ Ⓩⓢ ⓗⓏⓗ ⓡⓌ Ⓘ ⓓⓐ ⓓⓎ Ⓩⓢ ⓗⓏⓗ ⓡⓌ
- 107. ⒼⒸ⒪Ⓧⓚⓟ⒪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]; ⓡⓌ
- 108. ⒽⒸ⒪Ⓥⓖⓟ⒪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];