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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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Meta-description in JSON:
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\"n01883070 wombat\"\n0.0401 - \"n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis\"\n0.0158 - \"n02137549 mongoose\"\n0.0146 - \"n01877812 wallaby, brush kangaroo\"\n0.0134 - \"n02396427 wild boar, boar, Sus scrofa\"\n" } ], "prediction": "0.1033 - \"n04209239 shower curtain\"\n0.0699 - \"n02777292 balance beam, beam\"\n0.0464 - \"n04116512 rubber eraser, rubber, pencil eraser\"\n0.0419 - \"n03938244 pillow\"\n0.0388 - \"n02808304 bath towel\"\n", "time": [ 8005, 7914, 7503, 7669, 7935, 7458, 7624, 7366, 7281, 7314, 7804, 7900, 7492, 7414, 7623, 8331, 8089, 7939 ], "user": "", "xopenme": { "execution_time": [ 5.819036, 5.68581, 5.750164, 5.744244, 5.562625, 5.63999, 5.88433, 5.730133, 5.666847, 5.637824, 5.66574, 6.0775, 5.601574, 5.669847, 5.839874, 5.676965, 5.847486, 5.905751 ], "execution_time_kernel_0": [ 5.819036, 5.68581, 5.750164, 5.744244, 5.562625, 5.63999, 5.88433, 5.730133, 5.666847, 5.637824, 5.66574, 6.0775, 5.601574, 5.669847, 5.839874, 5.676965, 5.847486, 5.905751 ], "execution_time_kernel_1": [ 1.137113, 0.861433, 0.873667, 0.960609, 0.976831, 0.939766, 0.756049, 0.775446, 0.736054, 0.773697, 0.7626, 0.764812, 0.775702, 0.764719, 0.7828, 0.996072, 1.007028, 0.99959 ], "execution_time_kernel_2": [ 0.881113, 1.239845, 0.754493, 0.841358, 1.24918, 0.756466, 0.829146, 0.731272, 0.754074, 0.781704, 1.234154, 0.924241, 0.905603, 0.859098, 0.876303, 1.535867, 1.078528, 0.911483 ] } }, { "behavior_uid": "343abf444e58adb3", "cpu_freqs_after": [ { "0": 2016, "1": 2016, "2": 2016, "3": 2016, "4": 2016, "5": 2016, "6": 2016, "7": 2016 } ], "cpu_freqs_before": [ { "0": 2016, "1": 2016, "2": 2016, "3": 2016, "4": 2016, "5": 2016, "6": 2016, "7": 2016 } ], "image_height": 1108, "image_width": 1293, "prediction": "0.1479 - \"n03417042 garbage truck, dustcart\"\n0.0989 - \"n03345487 fire engine, fire truck\"\n0.0907 - \"n03769881 minibus\"\n0.0849 - \"n04065272 recreational vehicle, RV, R.V.\"\n0.0828 - \"n03670208 limousine, limo\"\n", "time": [ 6704, 6773, 6709 ], "user": "", "xopenme": { "execution_time": [ 5.651552, 5.703477, 5.642731 ], "execution_time_kernel_0": [ 5.651552, 5.703477, 5.642731 ], "execution_time_kernel_1": [ 0.070035, 0.080899, 0.075132 ], "execution_time_kernel_2": [ 0.856824, 0.873692, 0.865979 ] } }, { "behavior_uid": "ad165c3b4b9d882f", "cpu_freqs_after": [ { "0": 2016, "1": 2016, "2": 2016, "3": 2016, "4": 2016, "5": 2016, "6": 2016, "7": 2016 } ], "cpu_freqs_before": [ { "0": 2016, "1": 2016, "2": 2016, "3": 2016, "4": 2016, "5": 2016, "6": 2016, "7": 2016 } ], "image_height": 298, "image_width": 372, "prediction": "0.3646 - \"n03770679 minivan\"\n0.2253 - \"n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon\"\n0.1373 - \"n03100240 convertible\"\n0.0788 - \"n02930766 cab, hack, taxi, taxicab\"\n0.0625 - \"n02974003 car wheel\"\n", "time": [ 6605, 6510, 6542 ], "user": "", "xopenme": { "execution_time": [ 5.689488, 5.609409, 5.675867 ], "execution_time_kernel_0": [ 5.689488, 5.609409, 5.675867 ], "execution_time_kernel_1": [ 0.008882, 0.006367, 0.007993 ], "execution_time_kernel_2": [ 0.784564, 0.78396, 0.749337 ] } } ], "meta": { "cpu_abi": "arm64-v8a", "cpu_name": "Qualcomm Technologies, Inc MSM8953", "cpu_uid": "8a97c18b896434b3", "crowd_uid": "a7340ffbefcb5923", "engine": "Caffe CPU", "gpgpu_name": "", "gpgpu_uid": "", "gpu_name": "Qualcomm Adreno (TM) 506", "gpu_uid": "137fbb84761a2227", "model": "BVLC AlexNet", "os_name": "Android 7.0", "os_uid": "de98569847a92092", "plat_name": "ASUS Z012D", "platform_uid": "" } }
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Developed by
Grigori Fursin
Implemented as a
CK workflow
Hosted at