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(under development) auto/crowd-tune CUDA work size (execution time)
(under development) auto/crowd-tune OpenCL local work size (execution time)
(under development) auto/crowd-tune OpenCL local work size (execution time/FPS vs energy)
(under development) crowdsource OpenCL bug detection
(under development) crowdsource modeling of program behavior
(under development) crowdsource program numerical stability
(under development) crowdsource program scalability
Collaborative Program Optimization using mobile devices
advice
ae
ae.person.table
ai-artifact
algorithm
all
announcements.funding
announcements.job
apk
artifact
auto/crowd-tune GCC compiler flags (custom dimensions)
auto/crowd-tune GCC compiler flags (do not degrade execution time, do not degrade code size)
auto/crowd-tune GCC compiler flags (minimize execution time and code size)
auto/crowd-tune GCC compiler flags (minimize execution time)
auto/crowd-tune GCC compiler flags (minimize execution time, do not degrade code size)
auto/crowd-tune GCC compiler flags (minimize total binary size, do not degrade execution time)
auto/crowd-tune LLVM compiler flags (do not degrade execution time, do not degrade code size)
auto/crowd-tune LLVM compiler flags (minimize execution time)
auto/crowd-tune LLVM compiler flags (minimize execution time, do not degrade code size)
auto/crowd-tune OpenCL-based CLBlast (GFLOPs)
autotune custom pipeline dimensions
award
caffe
caffe2
cbricks
cfg
challenge.vqe
choice
class
clblast
cmdgen
compiler
crowd-benchmark DNN libraries and models
crowd-benchmark DNN libraries and models (Caffe - dev)
crowd-benchmark DNN libraries and models (Caffe2)
crowd-benchmark DNN libraries and models (TensorFlow)
crowd-benchmark DNN libraries and models (dividiti desktop app)
crowd-benchmark DNN libraries and models using mobile devices
crowd-benchmark shared workloads via ARM WA framework
crowd-test OpenCL compilers (beta) - crowdsource bug detection via CK
crowd-test OpenGL compilers (beta)
crowdnode
dashboard
dataset
dataset.features
demo
device (deprecated or not used)
dissemination.announcement
dissemination.book
dissemination.conference
dissemination.event
dissemination.hardware
dissemination.journal
dissemination.keynote
dissemination.lecture
dissemination.patent
dissemination.pitfall
dissemination.poster
dissemination.presentation
dissemination.press-release
dissemination.publication
dissemination.publication.artifact
dissemination.repo
dissemination.soft
dissemination.workshop
docker
env
experiment
experiment.raw
experiment.scenario.android
experiment.user
experiment.view
explore DNN batch size
explore GCC compiler flags
explore LLVM compiler flags
explore OpenBLAS number of threads
explore OpenMP number of threads
explore compiler flags
fuzz GCC compiler flags (search for bugs)
fuzz LLVM compiler flags (search for bugs)
gemmbench.crowdtuning
graph
graph.dot
hackathon.20180615
hackathon.20181006
hackathon.20190127
hackathon.20190315
index
jnotebook
kernel
log
machine
math.conditions
math.frontier
math.variation
me
milepost
misc
mlperf
mlperf.inference
mlperf.mobilenets
model
model.image.classification
model.r
model.sklearn
model.species
model.tensorflowapi
model.tf
module
nntest
open ReQuEST @ ASPLOS'18 tournament (Pareto-efficient image classification)
organization
os
package
person
photo
pipeline
pipeline.cmd
platform
platform.cpu
platform.dsp
platform.gpgpu
platform.gpu
platform.init
platform.nn
platform.npu
platform.os
proceedings.acm
program
program.behavior
program.dynamic.features
program.experiment.speedup
program.optimization
program.output
program.species
program.static.features
qml
qr-code
repo
report
research.topic
result
scc-workflow
script
slide
soft
solution
sut
table
tensorflow
test
tmp
user
video
vqe
wa
wa-device
wa-params
wa-result
wa-scenario
wa-tool
web
wfe
xml
Repository:
CK (machine learning based) multi-objective autotuning
CK analytics
CK crowdtuning (crowdsourcing autotuning)
CK dissemination modules
CK repository to crowdsource optimization of benchmarks, kernels and realistic workloads across Raspberry Pi devices provided by volunteers (starting from compiler flag autotuning)
CK web
Large and shared artifacts (HOG experiments) to reproduce CK paper
Reproducible and interactive papers with all shared artifacts for our CK papers
Reproducing PAMELA project (medium data set (20 frames) for slambench) via CK
Reproducing PAMELA project (slambench analysis and crowd-tuning) via CK
Tool clsmith converted to CK format
cTuning datasets (min)
cTuning programs
cbricks
ck-ai
ck-artifact-evaluation
ck-assets
ck-caffe
ck-caffe2
ck-cntk
ck-crowd-scenarios
ck-crowdsource-dnn-optimization
ck-crowdtuning-platforms
ck-dev-compilers
ck-dissemination
ck-docker
ck-env
ck-experiments
ck-graph-analytics
ck-math
ck-mlperf
ck-mlperf-sysml-demo-20190402
ck-mxnet
ck-nntest
ck-nntest-20181001
ck-qiskit
ck-quantum
ck-quantum-challenge-vqe
ck-quantum-hackathon-20180615
ck-quantum-hackathon-20181006
ck-quantum-hackathon-20190127
ck-quantum-hackathon-20190315
ck-quantum-hackathons
ck-request
ck-request-asplos18-caffe-intel
ck-request-asplos18-iot-farm
ck-request-asplos18-mobilenets-armcl-opencl
ck-request-asplos18-mobilenets-tvm-arm
ck-request-asplos18-resnet-tvm-fpga
ck-request-asplos18-results
ck-request-asplos18-results-caffe-intel
ck-request-asplos18-results-iot-farm
ck-request-asplos18-results-mobilenets-armcl-opencl
ck-request-asplos18-results-mobilenets-tvm-arm
ck-request-asplos18-results-resnet-tvm-fpga
ck-rigetti
ck-rpi-optimization-results
ck-scc
ck-scc18
ck-tensorflow
ck-tensorrt
ck-tvm
ck-wa
ck-wa-extra
ck-wa-workloads
ck-website
ctuning-datasets
default
gemmbench
local
mlperf-mobilenets
reproduce-carp-project
reproduce-milepost-project
shader-compiler-bugs
upload
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Meta-description in JSON:
{ "all_raw_results": [ { "behavior_uid": "e0d9b54cc39509c5", "cpu_freqs_after": [ { "0": 1300, "1": 1300, "2": 1300, "3": 1300 } ], "cpu_freqs_before": [ { "0": 1300, "1": 1300, "2": 1300, "3": 1300 } ], "image_height": 1000, "image_width": 720, "prediction": "0.6626 - \"n02123045 tabby, tabby cat\"\n0.1602 - \"n02124075 Egyptian cat\"\n0.1508 - \"n02123159 tiger cat\"\n0.0031 - \"n04033995 quilt, comforter, comfort, puff\"\n0.0018 - \"n04074963 remote control, remote\"\n", "time": [ 41948, 32115, 26835 ], "user": "", "xopenme": { "execution_time": [ 3.599317, 3.591766, 3.556468 ], "execution_time_kernel_0": [ 3.599317, 3.591766, 3.556468 ], "execution_time_kernel_1": [ 0.093602, 0.092399, 0.092128 ], "execution_time_kernel_2": [ 38.131433, 28.32307, 23.068303 ] } }, { "behavior_uid": "3c7f2449bcbbd1ca", "cpu_freqs_after": [ { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 } ], "cpu_freqs_before": [ { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 } ], "image_height": 1872, "image_width": 2600, "mispredictions": [ { "correct_answer": "fish, roach, rudd", "mispredicted_image": "misprediction-image-46beab673dd5f262.jpg", "misprediction_results": "0.5429 - \"n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch\"\n0.2693 - \"n02454379 armadillo\"\n0.0682 - \"n02514041 barracouta, snoek\"\n0.0659 - \"n01440764 tench, Tinca tinca\"\n0.0107 - \"n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus\"\n" }, { "correct_answer": "tiring-irons", "mispredicted_image": "misprediction-image-c0caf8b1c790bdba.jpg", "misprediction_results": "0.8108 - \"n04127249 safety pin\"\n0.0898 - \"n03476684 hair slide\"\n0.0240 - \"n03804744 nail\"\n0.0226 - \"n04153751 screw\"\n0.0106 - \"n03532672 hook, claw\"\n" }, { "correct_answer": "fish", "mispredicted_image": "misprediction-image-3a41b391d05a4388.jpg", "misprediction_results": "0.7831 - \"n13040303 stinkhorn, carrion fungus\"\n0.0295 - \"n01440764 tench, Tinca tinca\"\n0.0184 - \"n02514041 barracouta, snoek\"\n0.0180 - \"n13044778 earthstar\"\n0.0159 - \"n12998815 agaric\"\n" }, { "correct_answer": "building", "mispredicted_image": "misprediction-image-9b2aa64d1add18ef.jpg", "misprediction_results": "0.2090 - \"n03877845 palace\"\n0.0568 - \"n09332890 lakeside, lakeshore\"\n0.0394 - \"n03976657 pole\"\n0.0373 - \"n04149813 scoreboard\"\n0.0353 - \"n03661043 library\"\n" }, { "correct_answer": "passenger train", "mispredicted_image": "misprediction-image-bfb4038a04f3967e.jpg", "misprediction_results": "0.9741 - \"n03895866 passenger car, coach, carriage\"\n0.0127 - \"n04487081 trolleybus, trolley coach, trackless trolley\"\n0.0093 - \"n04335435 streetcar, tram, tramcar, trolley, trolley car\"\n0.0015 - \"n02917067 bullet train, bullet\"\n0.0003 - \"n03494278 harmonica, mouth organ, harp, mouth harp\"\n" }, { "correct_answer": "rainbow", "mispredicted_image": "misprediction-image-61c8ed4f441f43a4.jpg", "misprediction_results": "0.8220 - \"n03344393 fireboat\"\n0.1476 - \"n03388043 fountain\"\n0.0083 - \"n04311004 steel arch bridge\"\n0.0037 - \"n03888257 parachute, chute\"\n0.0032 - \"n09288635 geyser\"\n" }, { "correct_answer": "sunset", "mispredicted_image": "misprediction-image-825b2f43c4355ad2.jpg", "misprediction_results": "0.5525 - \"n09428293 seashore, coast, seacoast, sea-coast\"\n0.2720 - \"n09332890 lakeside, lakeshore\"\n0.0626 - \"n09421951 sandbar, sand bar\"\n0.0357 - \"n09399592 promontory, headland, head, foreland\"\n0.0192 - \"n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty\"\n" }, { "correct_answer": "sunrise", "mispredicted_image": "misprediction-image-7868181ecc6ece8c.jpg", "misprediction_results": "0.3277 - \"n09428293 seashore, coast, seacoast, sea-coast\"\n0.1918 - \"n09332890 lakeside, lakeshore\"\n0.1352 - \"n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty\"\n0.0728 - \"n09288635 geyser\"\n0.0535 - \"n09421951 sandbar, sand bar\"\n" }, { "correct_answer": "flowering tree, blossoming tree, blooming tree", "mispredicted_image": "misprediction-image-6a341aab5878b232.jpg", "misprediction_results": "0.3961 - \"n03891251 park bench\"\n0.2738 - \"n03743016 megalith, megalithic structure\"\n0.0692 - \"n03733281 maze, labyrinth\"\n0.0508 - \"n04326547 stone wall\"\n0.0462 - \"n11879895 rapeseed\"\n" } ], "prediction": "0.9730 - \"n07745940 strawberry\"\n0.0201 - \"n04476259 tray\"\n0.0016 - \"n07768694 pomegranate\"\n0.0007 - \"n03991062 pot, flowerpot\"\n0.0006 - \"n12620546 hip, rose hip, rosehip\"\n", "time": [ 44484, 41545, 43061, 44397, 39141, 27762, 42458, 112910, 66506, 25534, 24763, 25320, 23800, 26568, 24504, 25827, 25100, 25043, 23778, 23915, 23625, 25462, 24071, 24018, 26328, 28269, 25912, 24166, 24121, 23454, 25687, 24513, 24586, 25101, 25037, 24934, 24974, 26626, 25294 ], "user": "", "xopenme": { "execution_time": [ 3.971429, 3.724233, 3.841011, 3.607645, 3.669155, 3.564002, 3.618407, 3.538091, 6.099259, 5.16135, 3.925621, 5.051871, 3.75412, 4.354927, 4.445959, 4.701507, 5.183963, 5.167535, 3.575341, 3.85525, 3.599845, 3.572619, 3.550383, 3.542512, 5.331603, 4.78735, 5.040767, 3.841616, 3.668384, 3.645557, 3.54505, 3.519841, 3.560048, 5.202956, 5.194114, 5.056657, 4.838452, 4.952483, 5.278355 ], "execution_time_kernel_0": [ 3.971429, 3.724233, 3.841011, 3.607645, 3.669155, 3.564002, 3.618407, 3.538091, 6.099259, 5.16135, 3.925621, 5.051871, 3.75412, 4.354927, 4.445959, 4.701507, 5.183963, 5.167535, 3.575341, 3.85525, 3.599845, 3.572619, 3.550383, 3.542512, 5.331603, 4.78735, 5.040767, 3.841616, 3.668384, 3.645557, 3.54505, 3.519841, 3.560048, 5.202956, 5.194114, 5.056657, 4.838452, 4.952483, 5.278355 ], "execution_time_kernel_1": [ 0.558424, 0.532216, 0.534737, 0.572906, 0.559383, 0.566409, 0.576667, 0.570419, 0.865016, 0.55894, 0.563813, 0.55258, 0.541344, 0.556612, 0.541355, 0.517377, 0.515175, 0.514838, 0.585497, 0.581366, 0.591271, 0.542667, 0.535355, 0.532273, 0.488102, 0.476924, 0.519359, 0.506036, 0.520222, 0.507541, 0.538431, 0.531185, 0.531298, 0.497482, 0.481703, 0.49143, 0.618283, 0.614311, 0.605761 ], "execution_time_kernel_2": [ 39.761304, 37.14773, 38.548403, 40.089489, 34.799626, 23.507923, 38.124183, 108.638812, 59.349776, 19.677014, 20.110897, 19.525602, 19.385037, 21.545402, 19.398905, 20.471379, 19.283882, 19.242242, 19.498882, 19.371649, 19.326442, 21.221635, 19.875795, 19.835796, 20.377324, 22.886773, 20.243455, 19.691577, 19.811366, 19.194506, 21.482501, 20.35511, 20.380996, 19.273123, 19.254911, 19.280778, 19.387824, 20.947873, 19.292613 ] } }, { "behavior_uid": "f104bb2970786120", "cpu_freqs_after": [ { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 } ], "cpu_freqs_before": [ { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 } ], "image_height": 2560, "image_width": 1920, "mispredictions": [ { "correct_answer": "British shorthair cat", "mispredicted_image": "misprediction-image-01a57089e7423aa5.jpg", "misprediction_results": "0.3579 - \"n02124075 Egyptian cat\"\n0.1583 - \"n02808304 bath towel\"\n0.1158 - \"n02123597 Siamese cat, Siamese\"\n0.0665 - \"n02127052 lynx, catamount\"\n0.0381 - \"n02123045 tabby, tabby cat\"\n" } ], "prediction": "0.3579 - \"n02124075 Egyptian cat\"\n0.1583 - \"n02808304 bath towel\"\n0.1158 - \"n02123597 Siamese cat, Siamese\"\n0.0665 - \"n02127052 lynx, catamount\"\n0.0381 - \"n02123045 tabby, tabby cat\"\n", "time": [ 25200, 24888, 24558, 25116, 26056, 24237, 24313, 24428, 23579, 24082, 23374, 25610 ], "user": "", "xopenme": { "execution_time": [ 5.312156, 5.004866, 4.668694, 4.997456, 5.191947, 3.935248, 3.700663, 3.528368, 3.532645, 3.575489, 3.544246, 3.679957 ], "execution_time_kernel_0": [ 5.312156, 5.004866, 4.668694, 4.997456, 5.191947, 3.935248, 3.700663, 3.528368, 3.532645, 3.575489, 3.544246, 3.679957 ], "execution_time_kernel_1": [ 0.498816, 0.509365, 0.489971, 0.506703, 0.500367, 0.536845, 0.504368, 0.520171, 0.516836, 0.505983, 0.50778, 0.562413 ], "execution_time_kernel_2": [ 19.271041, 19.256378, 19.293155, 19.490041, 20.253138, 19.642232, 19.988359, 20.268404, 19.409467, 19.883089, 19.212632, 21.258199 ] } }, { "behavior_uid": "4c7e4f43a8da2f46", "cpu_freqs_after": [ { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 } ], "cpu_freqs_before": [ { "0": 1300, "1": 1300, "2": 1300, "3": 1300 }, { "0": 1300, "1": 1300, "2": 1300, "3": 1300 } ], "image_height": 1872, "image_width": 2236, "mispredictions": [ { "correct_answer": "british shorthair cat", "mispredicted_image": "misprediction-image-0d5816705477bbcc.jpg", "misprediction_results": "0.5493 - \"n02138441 meerkat, mierkat\"\n0.1853 - \"n02137549 mongoose\"\n0.0624 - \"n01664065 loggerhead, loggerhead turtle, Caretta caretta\"\n0.0274 - \"n02655020 puffer, pufferfish, blowfish, globefish\"\n0.0236 - \"n02317335 starfish, sea star\"\n" } ], "prediction": "0.5493 - \"n02138441 meerkat, mierkat\"\n0.1853 - \"n02137549 mongoose\"\n0.0624 - \"n01664065 loggerhead, loggerhead turtle, Caretta caretta\"\n0.0274 - \"n02655020 puffer, pufferfish, blowfish, globefish\"\n0.0236 - \"n02317335 starfish, sea star\"\n", "time": [ 25553, 23692, 23573, 25186, 24991, 24878 ], "user": "", "xopenme": { "execution_time": [ 3.596578, 3.634894, 3.512297, 5.075462, 5.094571, 4.981529 ], "execution_time_kernel_0": [ 3.596578, 3.634894, 3.512297, 5.075462, 5.094571, 4.981529 ], "execution_time_kernel_1": [ 0.478751, 0.46567, 0.460664, 0.460604, 0.457911, 0.453273 ], "execution_time_kernel_2": [ 21.297231, 19.465648, 19.480669, 19.517431, 19.329285, 19.338707 ] } } ], "meta": { "cpu_abi": "armeabi-v7a", "cpu_name": "MT6582", "cpu_uid": "ec41549029ef6e2f", "crowd_uid": "947b0f4e546c6ae5", "engine": "Caffe CPU", "gpgpu_name": "", "gpgpu_uid": "", "gpu_name": "ARM Mali-400 MP", "gpu_uid": "445a5b4fa3ff2cb0", "model": "BVLC GoogleNet", "os_name": "Android 5.0.2", "os_uid": "bb6cce787ea5e178", "plat_name": "LENOVO A5000", "platform_uid": "" } }
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Developed by
Grigori Fursin
Implemented as a
CK workflow
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