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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
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{ "all_raw_results": [ { "behavior_uid": "4eb48d899f201790", "cpu_freqs_after": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "cpu_freqs_before": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "image_height": 4032, "image_width": 3024, "mispredictions": [ { "correct_answer": "dryer", "mispredicted_image": "misprediction-image-9fccae7344bb59d1.jpg", "misprediction_results": "0.2803 - \"n04517823 vacuum, vacuum cleaner\"\n0.1344 - \"n03709823 mailbag, postbag\"\n0.1082 - \"n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack\"\n0.1016 - \"n04026417 purse\"\n0.0703 - \"n04317175 stethoscope\"\n" } ], "prediction": "0.2803 - \"n04517823 vacuum, vacuum cleaner\"\n0.1344 - \"n03709823 mailbag, postbag\"\n0.1082 - \"n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack\"\n0.1016 - \"n04026417 purse\"\n0.0703 - \"n04317175 stethoscope\"\n", "time": [ 4980, 4956, 5171, 1308, 1179, 1263, 1840, 1383, 1318, 1286, 1266, 1157 ], "user": "", "xopenme": { "execution_time": [ 0.197456, 0.196032, 0.199066, 0.06963, 0.067883, 0.065139, 0.067274, 0.074083, 0.07239, 0.065916, 0.06343, 0.064065 ], "execution_time_kernel_0": [ 0.197456, 0.196032, 0.199066, 0.06963, 0.067883, 0.065139, 0.067274, 0.074083, 0.07239, 0.065916, 0.06343, 0.064065 ], "execution_time_kernel_1": [ 0.736372, 0.741059, 0.818964, 0.272528, 0.258381, 0.260005, 0.246563, 0.272516, 0.246468, 0.23316, 0.230778, 0.230515 ], "execution_time_kernel_2": [ 3.96278, 3.936919, 4.067009, 0.911958, 0.808071, 0.894909, 1.484688, 0.993668, 0.949376, 0.947945, 0.933558, 0.824071 ] } }, { "behavior_uid": "4b1ff484cc44585f", "cpu_freqs_after": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "cpu_freqs_before": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "image_height": 2592, "image_width": 1458, "prediction": "0.1075 - \"n03877472 pajama, pyjama, pj's, jammies\"\n0.1072 - \"n04162706 seat belt, seatbelt\"\n0.0630 - \"n02786058 Band Aid\"\n0.0556 - \"n03617480 kimono\"\n0.0511 - \"n02834397 bib\"\n", "time": [ 1563, 1225, 1068 ], "user": "", "xopenme": { "execution_time": [ 0.063065, 0.066949, 0.066306 ], "execution_time_kernel_0": [ 0.063065, 0.066949, 0.066306 ], "execution_time_kernel_1": [ 0.074486, 0.078639, 0.080874 ], "execution_time_kernel_2": [ 1.389422, 1.042142, 0.881388 ] } }, { "behavior_uid": "58ef22d2e54eaeb2", "cpu_freqs_after": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "cpu_freqs_before": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "image_height": 3024, "image_width": 3528, "prediction": "0.7782 - \"n04263257 soup bowl\"\n0.0537 - \"n03775546 mixing bowl\"\n0.0472 - \"n07584110 consomme\"\n0.0456 - \"n07590611 hot pot, hotpot\"\n0.0270 - \"n07583066 guacamole\"\n", "time": [ 1607, 1143, 1310 ], "user": "", "xopenme": { "execution_time": [ 0.074757, 0.085659, 0.07117 ], "execution_time_kernel_0": [ 0.074757, 0.085659, 0.07117 ], "execution_time_kernel_1": [ 0.247744, 0.231075, 0.231256 ], "execution_time_kernel_2": [ 1.244195, 0.785578, 0.966867 ] } }, { "behavior_uid": "1b3c44dbb45e529f", "cpu_freqs_after": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "cpu_freqs_before": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "image_height": 3150, "image_width": 2268, "prediction": "0.2605 - \"n04141975 scale, weighing machine\"\n0.1824 - \"n03179701 desk\"\n0.0933 - \"n03642806 laptop, laptop computer\"\n0.0728 - \"n03832673 notebook, notebook computer\"\n0.0627 - \"n03584829 iron, smoothing iron\"\n", "time": [ 1265, 1294, 1296 ], "user": "", "xopenme": { "execution_time": [ 0.065075, 0.071415, 0.069394 ], "execution_time_kernel_0": [ 0.065075, 0.071415, 0.069394 ], "execution_time_kernel_1": [ 0.145461, 0.145923, 0.151862 ], "execution_time_kernel_2": [ 1.016609, 1.032701, 1.033332 ] } }, { "behavior_uid": "f649680918d54d1d", "cpu_freqs_after": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "cpu_freqs_before": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "image_height": 1000, "image_width": 720, "prediction": "0.8916 - \"n03485407 hand-held computer, hand-held microcomputer\"\n0.0834 - \"n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone\"\n0.0060 - \"n04074963 remote control, remote\"\n0.0052 - \"n03642806 laptop, laptop computer\"\n0.0050 - \"n03832673 notebook, notebook computer\"\n", "time": [ 1100, 996, 1018, 1146, 1303, 1182, 1618, 1492, 1203, 1598, 1700, 1433 ], "user": "", "xopenme": { "execution_time": [ 0.063011, 0.069632, 0.071554, 0.065877, 0.072302, 0.07542, 0.076455, 0.072204, 0.085346, 0.070045, 0.073138, 0.071653 ], "execution_time_kernel_0": [ 0.063011, 0.069632, 0.071554, 0.065877, 0.072302, 0.07542, 0.076455, 0.072204, 0.085346, 0.070045, 0.073138, 0.071653 ], "execution_time_kernel_1": [ 0.015147, 0.015513, 0.014932, 0.015157, 0.015166, 0.015281, 0.023428, 0.015452, 0.019751, 0.01542, 0.015408, 0.015979 ], "execution_time_kernel_2": [ 0.988225, 0.874234, 0.894003, 1.032144, 1.179671, 1.054287, 1.478138, 1.359201, 1.058075, 1.4691, 1.562327, 1.301935 ] } }, { "behavior_uid": "d54a4eab8dc30e50", "cpu_freqs_after": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "cpu_freqs_before": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "image_height": 2000, "image_width": 1440, "prediction": "0.8066 - \"n04517823 vacuum, vacuum cleaner\"\n0.0749 - \"n03584829 iron, smoothing iron\"\n0.0229 - \"n03297495 espresso maker\"\n0.0085 - \"n02988304 CD player\"\n0.0080 - \"n04560804 water jug\"\n", "time": [ 1519, 1842, 1785 ], "user": "", "xopenme": { "execution_time": [ 0.06977, 0.067468, 0.076989 ], "execution_time_kernel_0": [ 0.06977, 0.067468, 0.076989 ], "execution_time_kernel_1": [ 0.052304, 0.057551, 0.0733 ], "execution_time_kernel_2": [ 1.356027, 1.673646, 1.586928 ] } }, { "behavior_uid": "b77ce668cd9740d0", "cpu_freqs_after": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "cpu_freqs_before": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "image_height": 1304, "image_width": 1304, "mispredictions": [ { "correct_answer": "headphone", "mispredicted_image": "misprediction-image-7b195643b7a28d22.jpg", "misprediction_results": "0.1769 - \"n04554684 washer, automatic washer, washing machine\"\n0.1354 - \"n04317175 stethoscope\"\n0.1182 - \"n03271574 electric fan, blower\"\n0.0542 - \"n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier\"\n0.0430 - \"n03065424 coil, spiral, volute, whorl, helix\"\n" } ], "prediction": "0.3396 - \"n07930864 cup\"\n0.2979 - \"n03775546 mixing bowl\"\n0.2803 - \"n03063599 coffee mug\"\n0.0085 - \"n03786901 mortar\"\n0.0083 - \"n03950228 pitcher, ewer\"\n", "time": [ 1676, 1646, 2617, 1630, 1607, 1742, 1878, 1543, 1564 ], "user": "", "xopenme": { "execution_time": [ 0.068586, 0.069666, 0.073929, 0.07573, 0.078529, 0.081029, 0.067269, 0.077303, 0.069391 ], "execution_time_kernel_0": [ 0.068586, 0.069666, 0.073929, 0.07573, 0.078529, 0.081029, 0.067269, 0.077303, 0.069391 ], "execution_time_kernel_1": [ 0.031012, 0.032369, 0.037757, 0.037241, 0.0376, 0.036183, 0.035998, 0.036068, 0.036113 ], "execution_time_kernel_2": [ 1.536982, 1.504517, 2.460568, 1.472803, 1.443204, 1.573798, 1.728928, 1.388238, 1.415457 ] } }, { "behavior_uid": "e947467fafe59eac", "cpu_freqs_after": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "cpu_freqs_before": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "image_height": 1369, "image_width": 1304, "prediction": "0.4206 - \"n03063599 coffee mug\"\n0.3594 - \"n07930864 cup\"\n0.0677 - \"n03063689 coffeepot\"\n0.0563 - \"n04560804 water jug\"\n0.0324 - \"n04254120 soap dispenser\"\n", "time": [ 1695, 1592, 1782 ], "user": "", "xopenme": { "execution_time": [ 0.070491, 0.073736, 0.072896 ], "execution_time_kernel_0": [ 0.070491, 0.073736, 0.072896 ], "execution_time_kernel_1": [ 0.034591, 0.03397, 0.043494 ], "execution_time_kernel_2": [ 1.547813, 1.440074, 1.606122 ] } }, { "behavior_uid": "0b57e2fa3dd56a9f", "cpu_freqs_after": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "cpu_freqs_before": [ { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600 }, { "0": 1586, "1": 1586, "2": 1586, "3": 1586, "4": 2600, "5": 2600, "6": 2600, "7": 2600 } ], "image_height": 790, "image_width": 1049, "prediction": "0.7858 - \"n04285008 sports car, sport car\"\n0.1070 - \"n03100240 convertible\"\n0.0626 - \"n04037443 racer, race car, racing car\"\n0.0153 - \"n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon\"\n0.0079 - \"n03459775 grille, radiator grille\"\n", "time": [ 1613, 1722, 1921, 2977, 1768, 1951 ], "user": "", "xopenme": { "execution_time": [ 0.067156, 0.072623, 0.073574, 0.106169, 0.072415, 0.071598 ], "execution_time_kernel_0": [ 0.067156, 0.072623, 0.073574, 0.106169, 0.072415, 0.071598 ], "execution_time_kernel_1": [ 0.018877, 0.018853, 0.018764, 0.024539, 0.019107, 0.023712 ], "execution_time_kernel_2": [ 1.487223, 1.589071, 1.761996, 2.79562, 1.63062, 1.813766 ] } } ], "meta": { "cpu_abi": "arm64-v8a", "cpu_name": "0x53-8-0x1-0x001-1", "cpu_uid": "64309bc9f07cf7bc", "crowd_uid": "0952cf7bb34139af", "engine": "Caffe CPU", "gpgpu_name": "", "gpgpu_uid": "", "gpu_name": "ARM Mali-T880", "gpu_uid": "49d08a05c0436cb8", "model": "DeepScale SqueezeNet 1.0", "os_name": "Android 7.0", "os_uid": "de98569847a92092", "plat_name": "SAMSUNG SM-G930S", "platform_uid": "" } }
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Developed by
Grigori Fursin
Implemented as a
CK workflow
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