Difference between revisions of "CM:data:45741e3fbcf4024b:1db78910464c9d05"

Latest revision as of 13:23, 4 September 2014

Computational species "bw filter simplified less" (CID=45741e3fbcf4024b:1db78910464c9d05)

Notes

This computation species (kernel) is a threshold filter (CID=45741e3fbcf4024b:1db78910464c9d05) - it is used in image processing and neuron activation functions (part of artificial neural networks).

Some cost-aware experiments (execution time, size, energy, compilation time) performed by Grigori Fursin using Collective Mind framework and artifacts from the public repository to be reproducible. It supports our collaborative research on continuous performance tracking, code optimization and compiler benchmarking (regression detection). If you find any mistakes or would like to extend this page, please help us!

cM repositories online:

Part1: download repo from github , browse repo at cMind , view nteractive graphs at cMind
Part2: download repo from github , browse repo at cMind , view interactive graphs at cMind
Part3: download repo from github , browse repo at cMind , view interactive graphs at cMind

Used artifacts

Datasets

D1 = grayscale image 1 (CID=8a7141c59cd335f5:c8848a1b1fb1775e), size=1536x1536~2.4E6 (pixels or neurons)
D2 = grayscale image 2 (CID=8a7141c59cd335f5:0045c9b59e84318b), size=1536x1536~2.4E6 (pixels or neurons)

Systems

S1 = Dell Laptop Latitude E6320, Processor=P1, Memory = 8Gb, Storage=256Gb (SSD), Max power consumption=52W, Cost (year of purchase 2011)~1200 euros (CID=cb7e6b406491a11c:0d84339816de0271)
S2 = Samsung Mobile Galaxy Duos GT-S6312, Processor=P2, Memory = 0.8Gb, Storage=4Gb, Battery=1300 mAh / 3.9V / up to 250 hours, Max power consumption~5W, Cost (year of purchase 2013)~200 euros (CID=cb7e6b406491a11c:a9740acbe06bcd1e)
S3 = Polaroid Tablet Executive 9" MID0927, Processor=P3, Memory=1Gb, Storage=16Gb, Battery=3500 mAh / 3.9V / up to 80 hours, Max power consumption~13W, Cost (year of purchase 2014)~100 euros (CID=cb7e6b406491a11c:3419444faf22f3d0)
S4 = Semiconductor neural network, PSpice simulation (year of development = 1993-1997)

Processors

P1 = Intel Core i5-2540M, 2.60GHz, 2 cores (CID=54cd38490124ef51:425ae4e3483c82e8)
P2 = Qualcomm MSM7625A FFA, ARM Cortex A5, ARMv7, 1 GHz, 1 core(CID=54cd38490124ef51:ae17889f40209ae7)
P3 = Allwinner A20 (sun7i), Dual-Core ARM Cortex A7, ARMv7, 1.6GHz, Mali400 GPU, 2 core (CID=54cd38490124ef51:fc020ce2e4d44f3d)
P4 = NVidia Quadro NVS 135M, 16 cores, 400MHz, 10Watt, 210 Million transistors (TBD)

Processor mode

W1 = 32 bit
W2 = 64 bit

OSs

O1 = Windows 7 Pro SP1, cost~170 euros (CID=c4d3ce728f46eea2:10c4f7484446b689)
O2 = O1, MinGW32
O2 = OpenSuse 12.1, Kernel 3.1.10, cost=free (CID=c4d3ce728f46eea2:29ce89f1a1446e89)
O3 = Android 4.1.2, Kernel 3.4.0, cost=free (CID=c4d3ce728f46eea2:e734c48d5a5824c1)
O4 = Android 4.2.2, Kernel 3.3.0, cost=free (CID=c4d3ce728f46eea2:d3e9b97f6994444b)

Compilers

X1 = GCC 4.1.1, number of optimization flags available~60+130, release date=2006 (CID=cff49b38f4c2395d:ac263305247d3953)
X2 = GCC 4.4.1, number of optimization flags available~100+170, release date=2009 (CID=cff49b38f4c2395d:15a583ed9eb54b57)
X3 = GCC 4.4.4, number of optimization flags available~100+170, release date=2010 (CID=0247b19de472d7d0:03a91d01a54ef7f5, CID=cff49b38f4c2395d:15a583ed9eb54b57)
X4 = GCC 4.6.3, number of optimization flags available~120+200 (including polyhedral and lto), release date=2012 (CID=0247b19de472d7d0:fc7b8424bbecc4d1, CID=cff49b38f4c2395d:2454492134ed4b73)
X5 = GCC 4.7.2, number of optimization flags available~130+210, release date=2012 (CID=0247b19de472d7d0:a1b38095ce254cd2, CID=cff49b38f4c2395d:9c1310b41c9a2b38)
X6 = GCC 4.8.3, number of optimization flags available~135+215, release date=2014 (CID=cff49b38f4c2395d:3474da936450dd7a)
X7 = GCC 4.9.1, number of optimization flags available~140+220, release date=2014 (CID=cff49b38f4c2395d:264156bb24190a99)
X8 = LLVM 3.1, number of optimization flags available=TBD, release date=2012 (CID=0247b19de472d7d0:697ad401bfc43c5b, CID=cff49b38f4c2395d:ad6b41ddae73bbd4)
X9 = LLVM 3.4.2, number of optimization flags available=TBD, release date=2014 (CID=cff49b38f4c2395d:b1e488aa91274cb6)
X10 = Open64 5.0, number of optimization flags available=TBD, release date=2011 (CID=0247b19de472d7d0:0599ca2a3b34a6b8, CID=cff49b38f4c2395d:48d5baa4569f59a8)
X11 = PathScale 2.3.1, number of optimization flags available=TBD, release date=2006 (CID=cff49b38f4c2395d:164c4aaff2b69279)
X12 = NVidia CUDA Toolkit 5.0, number of optimization flags available=TBD, release date=2012 (CID=0247b19de472d7d0:89e947f8430eaa37, CID=cff49b38f4c2395d:48d5baa4569f59a8)
X13 = Intel Composer XE 2011, number of optimization flags available=TBD, release date=2011, cost = ~800euro (CID=0247b19de472d7d0:e985f0596b1b1d9e, CID=cff49b38f4c2395d:42eab7eefa890ddc)
X14 = Microsoft Visual Studio 2013, number of optimization flags available=TBD, release date=2013,cost = has free minimal version (CID=cff49b38f4c2395d:5e35f4112bf996c5)

Compiler optimization level

Y1 = Performance (usually -O3)
Y2 = Size (usually -Os)
Y3 = -O3 -fmodulo-sched -funroll-all-loops
Y4 = -O3 -funroll-all-loops
Y5 = -O3 -fprefecth-loop-arrays
Y6 = -O3 -fno-if-conversion
Y7 = Auto-tuning with more than 6 flags (-fif-conversion)
Y8 = Auto-tuning with more than 6 flags (-fno-if-conversion)

Number of run-time code repetitions (for example, processing steps in neural networks)

R1 = 4000
R2 = 1000
R3 = 400

Total number of computations (processed neurons or pixels)

T1 ~ 9.6E9
T2 ~ 2.4E9
T3 ~ 1.0E9

Costs

C1= Execution time
C2 = Energy
C3 = Code size
C4 = Compilation time
C5 = System size
C6 = Hardware price
C7 = Software price
C8 = (Auto-)tuning price
C9 = Development time
C10 = Validation and testing time

Example of continuosly evolving advice (combination of decision trees and models)

def advice(i):

if i['usage_scenario']['need_fastest_code']=='yes':

if i['cost']['can_afford_specialized_hardware']=='yes':

i['advice']='S4'

else:

i['advice']='P4'

else:

if i['resources']['p1']['available']=='yes':

if i['resources']['p1']['cores']=='1':

if i['cost']['can_afford_auto_tuning']=='yes':

if i['dataset']['feature']=='D2':

i['advice']='Solution 11'

else:

i['advice']='Solution 4'

else:

i['advice']='Solution 5'

elif i['resources']['p1']['cores']=='2':

if i['cost']['can_afford_auto_tuning']=='yes':

i['advice']='Solution 7'

else:

i['advice']='Solution 6'

else:

i['advice']='Ask for more resources'

elif i['usage_scenario']['need_most_energy_efficient_code']=='yes':

if i['cost']['can_afford_specialized_hardware']=='yes':

i['advice']='P4'

else:

i['advice']='P2'

elif i['usage_scenario']['need_cheapest_hardware']=='yes':

i['advice']='P3'

elif i['usage_scenario']['need_smallest_code']=='yes':

i['advice']='Solution 9'

elif i['usage_scenario']['need_fastest_compilation_and_good_speed']=='yes':

i['advice']='X4'

return i

Notes

Energy: 1Wh = 3600 joules

W = mAh * V / 1000 = 1300 * 3.9 / 1000 ~ 5W

@@ Line 1: / Line 1: @@
 <p style="text-align: center"><span style="font-size:larger">Computational species '''"bw filter simplified less"''' ({{CREF|45741e3fbcf4024b:1db78910464c9d05}})</span></p>
-== Notes ==
+== Notes<br/> ==
-Some cost-aware experiments (execution time, size, energy) performed by {{FGG}}. It supports our research on code optimization and compiler benchmarking (regression detection).
-== Used artifacts ==
+''This computation species (kernel) is a threshold filter ({{CREF|45741e3fbcf4024b:1db78910464c9d05}}) - it is used in image processing and neuron activation functions (part of artificial neural networks).''
-*Datasets:
+Some cost-aware experiments (execution time, size, energy, compilation time) performed by {{FGG}} using [http://c-mind.org Collective Mind framework] and artifacts from the [http://c-mind.org/repo public repository] to be reproducible. It supports our collaborative research on continuous performance tracking, code optimization and compiler benchmarking (regression detection). If you find any mistakes or would like to extend this page, please help us!
-**D1 = image raw fgg office day gray ({{CREF|8a7141c59cd335f5:c8848a1b1fb1775e}})
-**D2 = image raw fgg office night gray ({{CREF|8a7141c59cd335f5:0045c9b59e84318b}})
-*Systes:
+cM repositories online:
-**S1 = Dell Latitude E6320, Memory = 8Gb, SSD=256Gb ({{CREF|cb7e6b406491a11c:0d84339816de0271}})
-**S2 = Samsung Galaxy Duos GT-S6312, Memory = 0.8Gb, Storage=4Gb, Battery=1300 mAh up to 250 hours ({{CREF|}})
-*Processors:
+*Part1: [https://github.com/gfursin/cm-experiments-201408xx-fgg-motivation-compiler-flag-tuning-mess-1 download repo from github] , [http://c-mind.org/repo/?cm_menu=browse&browse_module_uoa=ctuning.scenario.compiler.optimizations&browse_repo_uoa=cm-experiments-201408xx-fgg-motivation-compiler-flag-tuning-mess-1 browse repo at cMind] , [http://c-mind.org/repo/?cm_menu=scenarios&cm_submenu=ctuning_space_visualize&ct_module_uoa=ctuning.scenario.compiler.optimizations&ct_repo_uoa=cm-experiments-201408xx-fgg-motivation-compiler-flag-tuning-mess-1 view nteractive graphs at cMind]
-**P1 = Intel Core i5-2540M, 2.60GHz, 2 cores ({{CREF|}})
+*Part2: [https://github.com/gfursin/cm-experiments-201408xx-fgg-motivation-compiler-flag-tuning-mess-2 download repo from github] , [http://c-mind.org/repo/?cm_menu=browse&browse_module_uoa=ctuning.scenario.compiler.optimizations&browse_repo_uoa=cm-experiments-201408xx-fgg-motivation-compiler-flag-tuning-mess-2 browse repo at cMind] , [http://c-mind.org/repo/?cm_menu=scenarios&cm_submenu=ctuning_space_visualize&ct_module_uoa=ctuning.scenario.compiler.optimizations&ct_repo_uoa=cm-experiments-201408xx-fgg-motivation-compiler-flag-tuning-mess-2 view interactive graphs at cMind]
-**P2 = Qualcomm MSM7625A, ARM Cortex A5, ARMv7, 1 GHz, 1 core ({{CREF|}})
+*Part3: [https://github.com/gfursin/cm-experiments-201408xx-fgg-motivation-compiler-flag-tuning-mess-3 download repo from github] , [http://c-mind.org/repo/?cm_menu=browse&browse_module_uoa=ctuning.scenario.compiler.optimizations&browse_repo_uoa=cm-experiments-201408xx-fgg-motivation-compiler-flag-tuning-mess-3 browse repo at cMind] , [http://c-mind.org/repo/?cm_menu=scenarios&cm_submenu=ctuning_space_visualize&ct_module_uoa=ctuning.scenario.compiler.optimizations&ct_repo_uoa=cm-experiments-201408xx-fgg-motivation-compiler-flag-tuning-mess-3 view interactive graphs at cMind]
-*OSs:
+== Used artifacts<br/> ==
-**O1 = Windows 7 Pro SP1 ({{CREF|c4d3ce728f46eea2:10c4f7484446b689}})
-**O2 = OpenSuse 12.1, Kernel 3.1.10 ({{CREF|}})
-**O3 = Android 4.1.2, Kernel 3.4.0 ({{CREF|}})
-*Compilers:
+=== Datasets ===
-**LLVMXYZ = LLVM X.Y.Z
-**GCCXYZ = GCC X.Y.Z
+*D1 = grayscale image 1 ({{CREF|8a7141c59cd335f5:c8848a1b1fb1775e}}), size=1536x1536~2.4E6 (pixels or neurons)
-**MGCCXYZ = MingW X.Y.Z
+*D2 = grayscale image 2 ({{CREF|8a7141c59cd335f5:0045c9b59e84318b}}), size=1536x1536~2.4E6 (pixels or neurons)
-**SGCCXYZ = Sourcergy GCC X.Y.Z. for ARM
-**MX = Microsoft Visual Studio compilers X
+=== Systems ===
-**IX = Intel X
+*S1 = Dell Laptop Latitude E6320, Processor=P1, Memory = 8Gb, Storage=256Gb (SSD), Max power consumption=52W, Cost (year of purchase 2011)~1200 euros ({{CREF|cb7e6b406491a11c:0d84339816de0271}})
+*S2 = Samsung Mobile Galaxy Duos GT-S6312, Processor=P2, Memory = 0.8Gb, Storage=4Gb, Battery=1300 mAh / 3.9V / up to 250 hours, Max power consumption~5W, Cost (year of purchase 2013)~200 euros ({{CREF|cb7e6b406491a11c:a9740acbe06bcd1e}})
+*S3 = Polaroid Tablet Executive 9" MID0927, Processor=P3, Memory=1Gb, Storage=16Gb, Battery=3500 mAh / 3.9V / up to 80 hours, Max power consumption~13W, Cost (year of purchase 2014)~100 euros ({{CREF|cb7e6b406491a11c:3419444faf22f3d0}})
+*S4 = Semiconductor neural network, PSpice simulation (year of development = 1993-1997)
+=== Processors ===
+*P1 = Intel Core i5-2540M, 2.60GHz, 2 cores ({{CREF|54cd38490124ef51:425ae4e3483c82e8}})
+*P2 = Qualcomm MSM7625A FFA, ARM Cortex A5, ARMv7, 1 GHz, 1 core({{CREF|54cd38490124ef51:ae17889f40209ae7}})
+*P3 = Allwinner A20 (sun7i), Dual-Core ARM Cortex A7, ARMv7, 1.6GHz, Mali400 GPU, 2 core ({{CREF|54cd38490124ef51:fc020ce2e4d44f3d}})
+*P4 = NVidia Quadro NVS 135M, 16 cores, 400MHz, 10Watt, 210 Million transistors (TBD)
+=== Processor mode ===
+*W1 = 32 bit
+*W2 = 64 bit
+=== OSs ===
+*O1 = Windows 7 Pro SP1,&nbsp; cost~170 euros ({{CREF|c4d3ce728f46eea2:10c4f7484446b689}})
+*O2 = O1, MinGW32
+*O2 = OpenSuse 12.1, Kernel 3.1.10, cost=free ({{CREF|c4d3ce728f46eea2:29ce89f1a1446e89}})
+*O3 = Android 4.1.2, Kernel 3.4.0, cost=free ({{CREF|c4d3ce728f46eea2:e734c48d5a5824c1}})
+*O4 = Android 4.2.2, Kernel 3.3.0, cost=free ({{CREF|c4d3ce728f46eea2:d3e9b97f6994444b}})
+=== Compilers ===
+*X1 = '''GCC 4.1.1''', number of optimization flags available~60+130, release date=2006 ({{CREF|cff49b38f4c2395d:ac263305247d3953}})
+*X2 = '''GCC 4.4.1''', number of optimization flags available~100+170, release date=2009 ({{CREF|cff49b38f4c2395d:15a583ed9eb54b57}})
+*X3 = '''GCC 4.4.4''', number of optimization flags available~100+170, release date=2010 ({{CREF|0247b19de472d7d0:03a91d01a54ef7f5}}, {{CREF|cff49b38f4c2395d:15a583ed9eb54b57}})
+*X4 = '''GCC 4.6.3''', number of optimization flags available~120+200 (including polyhedral and lto), release date=2012 ({{CREF|0247b19de472d7d0:fc7b8424bbecc4d1}}, {{CREF|cff49b38f4c2395d:2454492134ed4b73}})
+*X5 = '''GCC 4.7.2''', number of optimization flags available~130+210, release date=2012 ({{CREF|0247b19de472d7d0:a1b38095ce254cd2}}, {{CREF|cff49b38f4c2395d:9c1310b41c9a2b38}})
+*X6 = '''GCC 4.8.3''', number of optimization flags available~135+215, release date=2014 ({{CREF|cff49b38f4c2395d:3474da936450dd7a}})
+*X7 = '''GCC 4.9.1''', number of optimization flags available~140+220, release date=2014 ({{CREF|cff49b38f4c2395d:264156bb24190a99}})
+*X8 = '''LLVM 3.1''', number of optimization flags available=TBD, release date=2012 ({{CREF|0247b19de472d7d0:697ad401bfc43c5b}}, {{CREF|cff49b38f4c2395d:ad6b41ddae73bbd4}})
+*X9 = '''LLVM 3.4.2''', number of optimization flags available=TBD, release date=2014 ({{CREF|cff49b38f4c2395d:b1e488aa91274cb6}})
+*X10 = '''Open64 5.0''', number of optimization flags available=TBD, release date=2011 ({{CREF|0247b19de472d7d0:0599ca2a3b34a6b8}}, {{CREF|cff49b38f4c2395d:48d5baa4569f59a8}})
+*X11 = '''PathScale 2.3.1''', number of optimization flags available=TBD, release date=2006 ({{CREF|cff49b38f4c2395d:164c4aaff2b69279}})
+*X12 = '''NVidia CUDA Toolkit 5.0''', number of optimization flags available=TBD, release date=2012 ({{CREF|0247b19de472d7d0:89e947f8430eaa37}}, {{CREF|cff49b38f4c2395d:48d5baa4569f59a8}})
+*X13 = '''Intel Composer XE 2011''', number of optimization flags available=TBD, release date=2011, cost = ~800euro ({{CREF|0247b19de472d7d0:e985f0596b1b1d9e}}, {{CREF|cff49b38f4c2395d:42eab7eefa890ddc}})
+*X14 = '''Microsoft Visual Studio 2013''', number of optimization flags available=TBD, release date=2013,cost = has free minimal version ({{CREF|cff49b38f4c2395d:5e35f4112bf996c5}})
+=== Compiler optimization level ===
+*Y1 = Performance (usually -O3)
+*Y2 = Size (usually -Os)
+*Y3 = -O3 -fmodulo-sched -funroll-all-loops
+*Y4 = -O3 -funroll-all-loops
+*Y5 = -O3 -fprefecth-loop-arrays
+*Y6 = -O3 -fno-if-conversion
+*Y7 = Auto-tuning with more than 6 flags (-fif-conversion)
+*Y8 = Auto-tuning with more than 6 flags (-fno-if-conversion)
+=== Number of run-time code repetitions (for example, processing steps in neural networks) ===
+*R1 = 4000
+*R2 = 1000
+*R3 = 400
+=== Total number of computations (processed neurons or pixels) ===
+*T1 ~ 9.6E9
+*T2 ~ 2.4E9
+*T3 ~ 1.0E9
+=== Costs ===
+*C1= Execution time
+*C2 = Energy
+*C3 = Code size
+*C4 = Compilation time
+*C5 = System size
+*C6 = Hardware price
+*C7 = Software price
+*C8 = (Auto-)tuning price
+*C9 = Development time
+*C10 = Validation and testing time
+== Example of continuosly evolving advice (combination of decision trees and models)<br/> ==
+def advice(i):
+:
+:if i['usage_scenario']['need_fastest_code']=='yes':
+::if i['cost']['can_afford_specialized_hardware']=='yes':
+:::if i['cost']['can_afford_specialized_hardware']=='yes':
+::::i['advice']='S4'
+:::else:
+::::i['advice']='P4'
+::else:
+:::if i['resources']['p1']['available']=='yes':
+::::if i['resources']['p1']['cores']=='1':
+:::::if i['cost']['can_afford_auto_tuning']=='yes':
+::::::if i['dataset']['feature']=='D2':
+:::::::i['advice']='Solution 11'
+::::::else:
+:::::::i['advice']='Solution 4'
+:::::else:
+::::::i['advice']='Solution 5'
+::::elif i['resources']['p1']['cores']=='2':
+:::::if i['cost']['can_afford_auto_tuning']=='yes':
+::::::i['advice']='Solution 7'
+:::::else:
+::::::i['advice']='Solution 6'
+:::else:
+::::i['advice']='Ask for more resources'
+:elif i['usage_scenario']['need_most_energy_efficient_code']=='yes':
+::if i['cost']['can_afford_specialized_hardware']=='yes':
+:::i['advice']='P4'
+::else:
+:::i['advice']='P2'
+:elif i['usage_scenario']['need_cheapest_hardware']=='yes':
+::i['advice']='P3'
+:elif i['usage_scenario']['need_smallest_code']=='yes':
+::i['advice']='Solution 9'
+:elif i['usage_scenario']['need_fastest_compilation_and_good_speed']=='yes':
+::i['advice']='X4'<br/>
+:
+:return i
+== Notes ==
+Energy: 1Wh = 3600 joules
-*Run-time kernel repetitions:
+W = mAh * V / 1000 = 1300 * 3.9 / 1000 ~ 5W
-**R1 = 4000
-**R2 = 400

Latest revision as of 13:23, 4 September 2014

Contents

Notes

Used artifacts

Datasets

Systems

Processors

Processor mode

OSs

Compilers

Compiler optimization level

Number of run-time code repetitions (for example, processing steps in neural networks)

Total number of computations (processed neurons or pixels)

Costs

Example of continuosly evolving advice (combination of decision trees and models)

Notes