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Evaluation #2

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opened 2018-01-24 18:14:25 +07:00 by brian · 6 comments
brian commented 2018-01-24 18:14:25 +07:00
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Neighbour: 6 neighbour, 10 samples
5/10 still yield good result, cite 2002
6/3 yields good result, cite last year

  • Control:
  • Inde:
  • Dep:

x axis / y axis

Computer:

  • HP Eliteone AIO
  • CentOS 7, Linux 3.10, x86-64
  • Intel(R) CoreTM i7-7700
  • 8 GB memory
  • Google Chrome 61.0.3163.79 (Official Build) (64-bit)

Computer:

  • Clevo-based laptop
  • Ubuntu Gnome 17.04, Linux 4.13, x86-64
  • Intel(R) CoreTM i7-6700HQ
  • 16 GB DDR4 memory (limited by browser)
  • Chromium 64.0.3282.119 (Official Build) (64-bit)

Computer used:

  • Dell AIO
  • CentOS 7, Linux 3.10, x86-64
  • Intel(R) CoreTM i5-3470S
  • 8 GB memory
  • Google Chrome 61.0.3163.79 (Official Build) (64-bit)

Memory: which part uses ram DONE

  • Control: Dataset
  • Inde: Algorithm phases (each at its best setting unless non-affecting)
  • Dep: Ram use
    Can cut out the last part?

How looks can affect interp DONE:

  • Control: Hybrid settings (enough to get good looks)
  • Inde: Dataset (small vs medium), multiple runs
  • Dep: Looks, when none of the class got selected

Values of refinement at the end of interp: Knows good refinement times, might depends on dataset

  • Control: Hybrid, dataset (large), settings
  • Inde: No of refinement at the end of interp
  • Dep: Time, looks, stress
    Note down the sweet spot.

Pivot hitrate: Show how good pivot is vs time spnent

  • Control: Hybrid, dataset (large), settings
  • Inde: No of pivot, multiple runs
  • Dep: Hit rate, non-hit dist difference, looks
    Note down the sweet spot.

Pivot time: Knows weather to bruteforce of pivot
10 runs, sufficient start
time spent finding parent (pivot and brute), time spent placing object, stress after / data size

How many full runs
10 runs, best settings so far
total time spent, looks, stress, average velo changes/ iterations

Multiple dataset: DONE
iterations / velocity changes, stress
Note down the sweet spot.
For hybrid only the end

Multiple dataset:
Dataset size / time

Talk about GC DONE
Talk about too much mem -> run time higher anyway. If u need fast, prolly use other languages.

Neighbour: 6 neighbour, 10 samples 5/10 still yield good result, cite 2002 6/3 yields good result, cite last year - Control: - Inde: - Dep: x axis / y axis Computer: - HP Eliteone AIO - CentOS 7, Linux 3.10, x86-64 - Intel(R) CoreTM i7-7700 - 8 GB memory - Google Chrome 61.0.3163.79 (Official Build) (64-bit) Computer: - Clevo-based laptop - Ubuntu Gnome 17.04, Linux 4.13, x86-64 - Intel(R) CoreTM i7-6700HQ - 16 GB DDR4 memory (limited by browser) - Chromium 64.0.3282.119 (Official Build) (64-bit) Computer **used**: - Dell AIO - CentOS 7, Linux 3.10, x86-64 - Intel(R) CoreTM i5-3470S - 8 GB memory - Google Chrome 61.0.3163.79 (Official Build) (64-bit) --------------------- Memory: which part uses ram **DONE** - Control: Dataset - Inde: Algorithm phases (each at its best setting unless non-affecting) - Dep: Ram use Can cut out the last part? --------------------- How looks can affect interp **DONE**: - Control: Hybrid settings (enough to get good looks) - Inde: Dataset (small vs medium), multiple runs - Dep: Looks, when none of the class got selected --------------------- Values of refinement at the end of interp: Knows good refinement times, might depends on dataset - Control: Hybrid, dataset (large), settings - Inde: No of refinement at the end of interp - Dep: Time, looks, stress Note down the sweet spot. --------------------- Pivot hitrate: Show how good pivot is vs time spnent - Control: Hybrid, dataset (large), settings - Inde: No of pivot, multiple runs - Dep: Hit rate, non-hit dist difference, looks Note down the sweet spot. --------------------- Pivot time: Knows weather to bruteforce of pivot 10 runs, sufficient start time spent finding parent (pivot and brute), time spent placing object, stress after / data size --------------------- How many full runs 10 runs, best settings so far total time spent, looks, stress, average velo changes/ iterations Multiple dataset: **DONE** iterations / velocity changes, stress Note down the sweet spot. For hybrid only the end Multiple dataset: Dataset size / time Talk about GC **DONE** Talk about too much mem -> run time higher anyway. If u need fast, prolly use other languages.
brian commented 2018-01-31 02:28:38 +07:00
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Neighbour sampling cache distances?
Poker 5000, no render

With cache
15994ms
calc takes 3945ms
force takes 9724ms
JS heap reach 125MB before simulation started
128MB after sim + gc

Without cache
12510ms
calc takes 1995ms
force takes 12203ms (calc included)
JS heap mem never reach 50MB even without manually invoking GC.

Conclusion: やめて くれ

Neighbour sampling cache distances? Poker 5000, no render With cache 15994ms calc takes 3945ms force takes 9724ms JS heap reach 125MB before simulation started 128MB after sim + gc Without cache 12510ms calc takes 1995ms force takes 12203ms (calc included) JS heap mem never reach 50MB even without manually invoking GC. **Conclusion: やめて くれ**
brian commented 2018-01-31 03:14:05 +07:00
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Link Poker 3000 no render

Default 42805 ms
Ram 747MB peak, 418 MB at the end with GC

Tweaked 38605 ms
Ram 492MB peak, 238 MB at the end with GC

Tweaked without adding links 37841 ms
Ram 95MB peak, 59 MB at the end with GC

Link Poker 3000 no render Default 42805 ms Ram 747MB peak, 418 MB at the end with GC Tweaked 38605 ms Ram 492MB peak, 238 MB at the end with GC Tweaked without adding links 37841 ms Ram 95MB peak, 59 MB at the end with GC
brian commented 2018-01-31 03:14:55 +07:00
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^ continue with default

^ continue with default
brian commented 2018-02-10 00:29:00 +07:00
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เล่าเรื่อง

  1. Metric ที่จะหยุด run:
    Stress วัดไม่ได้ นานเกิน
    Static number of iterations: not good
    Velocity changes เกี่ยวข้องกับ stress และ layout change
    ใช้ Velo change เข้าถึง threshold แล้วเลิก, ค่าขึ้นกับ algo, data

  2. Metric: Looks, Stress, Time, Memory
    Memory: ดู graph bottleneck
    Time: เพราะ hybrid เพื่อประหยัด neighbour ตอนจบ อาจจะ interp สวยนาน แต่สุดท้ายไม่ช่วย neighbour(show ด้วย)
    Looks เพราะ Stress ดูน้อยแต่ไม่ได้แปลว่าดูดี

  • Hybrid ข้าม neighbour ตอนจบไม่ได้ ดูแย่

  1. Test โลดด

  2. Conclude best practice

Other things to talk about:

  • Random ตอนแรก ดี/ไม่ดี
  • Interp ไม่ขยับ เห็น radial
  • Pivot hit rate
เล่าเรื่อง 1. Metric ที่จะหยุด run: Stress วัดไม่ได้ นานเกิน Static number of iterations: not good Velocity changes เกี่ยวข้องกับ stress และ layout change ใช้ Velo change เข้าถึง threshold แล้วเลิก, ค่าขึ้นกับ algo, data 2. Metric: Looks, Stress, Time, Memory Memory: ดู graph bottleneck Time: เพราะ hybrid เพื่อประหยัด neighbour ตอนจบ อาจจะ interp สวยนาน แต่สุดท้ายไม่ช่วย neighbour(show ด้วย) Looks เพราะ Stress ดูน้อยแต่ไม่ได้แปลว่าดูดี - Hybrid ข้าม neighbour ตอนจบไม่ได้ ดูแย่ 3. Test โลดด 4. Conclude best practice Other things to talk about: - Random ตอนแรก ดี/ไม่ดี - Interp ไม่ขยับ เห็น radial - Pivot hit rate
brian commented 2018-02-14 21:05:11 +07:00
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Neighbour

10,000 Until 0.65
34894.06 ms (58 its)
Post Stress: 0.3019698281499924

100,000 Until 0.65
670033.4650000001 ms (98 its)
699007.5100000001 ms (102 its)

**Neighbour** 10,000 Until 0.65 34894.06 ms (58 its) Post Stress: 0.3019698281499924 100,000 Until 0.65 670033.4650000001 ms (98 its) 699007.5100000001 ms (102 its)
brian commented 2018-02-16 00:52:37 +07:00
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Max data size:
470K neighbour is fine
600K neighbour ram full, swapping mem entire system. Hybrid without 3rd phase didn't.
1M crash on load

Max data size: 470K neighbour is fine 600K neighbour ram full, swapping mem entire system. Hybrid without 3rd phase didn't. 1M crash on load
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Reference: brian/d3-spring-model#2
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