diff --git a/bibliography.bib b/bibliography.bib
index 3194659066667db737d2471b37ff60f493de2890..6f7edd43d8cea15fe8e8ecb1bec7d374dd48e66d 100644
--- a/bibliography.bib
+++ b/bibliography.bib
@@ -1636,3 +1636,20 @@
pages = {70–78},
numpages = {9}
}
+
+@article{gregg2016flame,
+ author = {Gregg, Brendan},
+ title = {The Flame Graph},
+ date = {2016-05},
+ publisher = {Association for Computing Machinery},
+ address = {New York, NY, USA},
+ volume = 59,
+ number = 6,
+ issn = {0001-0782},
+ url = {https://doi.org/10.1145/2909476},
+ doi = {10.1145/2909476},
+ abstract = {This visualization of software execution is a new
+ necessity for performance profiling and debugging.},
+ journaltitle = {Communications of the ACM},
+ pages = {48–57},
+}
diff --git a/common.tex b/common.tex
index b03d9a47ca9dd4d7e36b40275bc90141b4f97c5a..fbea270c304320315b83989451fb346f98921626 100644
--- a/common.tex
+++ b/common.tex
@@ -473,6 +473,44 @@
}\%$
}
+\NewDocumentCommand{\fsCalcAvgDiskMedian}{O{fs/AvgDiskMedianResult}m}{
+ \drefcalc*[save=#1]{((d(fs/hdd/#2/duration_time:u/median) + d(fs/hdd-drop/#2/duration_time:u/median) + d(fs/ssd/#2/duration_time:u/median) + d(fs/ssd-drop/#2/duration_time:u/median)) / 4)}
+}
+
+\NewDocumentCommand{\fsAvgDiskMedian}{m}{
+ \fsCalcAvgDiskMedian{#1}
+ \drefformat{\drefresult} \unit{\second}
+}
+
+\NewDocumentCommand{\fsAvgDiskMedianImprovement}{mm}{
+ \fsCalcAvgDiskMedian[firstAvg]{#1}
+ \fsCalcAvgDiskMedian[secondAvg]{#2}
+ \drefcalc[percent]{(d(firstAvg) / d(secondAvg) - 1)}\%
+}
+
+\NewDocumentCommand{\fsCalcAvgNFSMedian}{O{fs/AvgNFSMedianResult}m}{
+ \drefcalc*[save=#1]{((d(fs/nfs/#2/duration_time:u/median) + d(fs/nfs-drop/#2/duration_time:u/median)) / 2)}
+}
+
+\NewDocumentCommand{\fsAvgNFSMedian}{m}{
+ \fsCalcAvgNFSMedian{#1}
+ \drefformat{\drefresult} \unit{\second}
+}
+
+\NewDocumentCommand{\fsAvgNFSMedianImprovement}{mm}{
+ \fsCalcAvgNFSMedian[firstAvg]{#1}
+ \fsCalcAvgNFSMedian[secondAvg]{#2}
+ \drefcalc[percent]{(d(firstAvg) / d(secondAvg) - 1)}\%
+}
+
+\NewDocumentCommand{\fsBothAvgMedians}{m}{
+ \fsAvgDiskMedian{#1} (\fsAvgNFSMedian{#1} on NFS)
+}
+
+\NewDocumentCommand{\fsBothAvgMedianImprovements}{mm}{
+ \fsAvgDiskMedianImprovement{#1}{#2} (\fsAvgNFSMedianImprovement{#1}{#2} on NFS)
+}
+
% commands used in our tikz figures
\newcommand{\notifymark}{\raisebox{0.5pt}{\tikz{\node[draw, inner sep=1, yshift=7, color=Orange, circle] at ($(w1Done) + (0.5 / 2, 0)$) {\scriptsize 2};}}}
diff --git a/data/fs-eval-results.tex b/data/fs-eval-results.tex
new file mode 100644
index 0000000000000000000000000000000000000000..d05f24e6a2506f819f07c338ee7e9dbd9c77327a
--- /dev/null
+++ b/data/fs-eval-results.tex
@@ -0,0 +1,3031 @@
+\drefset{hdd/emper-pipe-numa/duration_time:u/mean}{0.21222465585999997}
+\drefset{hdd/emper-pipe-numa/duration_time:u/std}{0.01154810412755873}
+\drefset{hdd/emper-pipe-numa/duration_time:u/min}{0.193021038}
+\drefset{hdd/emper-pipe-numa/duration_time:u/max}{0.25433529}
+\drefset{hdd/emper-pipe-numa/duration_time:u/median}{0.21199596050000002}
+\drefset{hdd/emper-pipe-numa/duration_time:u/upper_quartile}{0.21794738724999999}
+\drefset{hdd/emper-pipe-numa/duration_time:u/lower_quartile}{0.20456465375}
+\drefset{hdd/emper-pipe-numa/duration_time:u/lower_whisker}{0.193021038}
+\drefset{hdd/emper-pipe-numa/duration_time:u/upper_whisker}{0.228730822}
+\drefset{hdd/emper-io-notification-waitfd-no-comp/duration_time:u/mean}{0.25999495718000004}
+\drefset{hdd/emper-io-notification-waitfd-no-comp/duration_time:u/std}{0.011738883497416561}
+\drefset{hdd/emper-io-notification-waitfd-no-comp/duration_time:u/min}{0.23144699700000002}
+\drefset{hdd/emper-io-notification-waitfd-no-comp/duration_time:u/max}{0.285842584}
+\drefset{hdd/emper-io-notification-waitfd-no-comp/duration_time:u/median}{0.25817364350000005}
+\drefset{hdd/emper-io-notification-waitfd-no-comp/duration_time:u/upper_quartile}{0.26847176575}
+\drefset{hdd/emper-io-notification-waitfd-no-comp/duration_time:u/lower_quartile}{0.2521348965}
+\drefset{hdd/emper-io-notification-waitfd-no-comp/duration_time:u/lower_whisker}{0.23144699700000002}
+\drefset{hdd/emper-io-notification-waitfd-no-comp/duration_time:u/upper_whisker}{0.285842584}
+\drefset{hdd/go-parallel-walk/duration_time:u/mean}{1.46073060948}
+\drefset{hdd/go-parallel-walk/duration_time:u/std}{0.0340540671877092}
+\drefset{hdd/go-parallel-walk/duration_time:u/min}{1.3866273519999999}
+\drefset{hdd/go-parallel-walk/duration_time:u/max}{1.560971804}
+\drefset{hdd/go-parallel-walk/duration_time:u/median}{1.454335291}
+\drefset{hdd/go-parallel-walk/duration_time:u/upper_quartile}{1.4788985697500001}
+\drefset{hdd/go-parallel-walk/duration_time:u/lower_quartile}{1.43659017775}
+\drefset{hdd/go-parallel-walk/duration_time:u/lower_whisker}{1.3866273519999999}
+\drefset{hdd/go-parallel-walk/duration_time:u/upper_whisker}{1.535917868}
+\drefset{hdd/emper-io-stealing-pipe-no-comp-numa/duration_time:u/mean}{0.16694409734000001}
+\drefset{hdd/emper-io-stealing-pipe-no-comp-numa/duration_time:u/std}{0.00476688888264911}
+\drefset{hdd/emper-io-stealing-pipe-no-comp-numa/duration_time:u/min}{0.159135035}
+\drefset{hdd/emper-io-stealing-pipe-no-comp-numa/duration_time:u/max}{0.18176324600000002}
+\drefset{hdd/emper-io-stealing-pipe-no-comp-numa/duration_time:u/median}{0.16613755200000002}
+\drefset{hdd/emper-io-stealing-pipe-no-comp-numa/duration_time:u/upper_quartile}{0.169047307}
+\drefset{hdd/emper-io-stealing-pipe-no-comp-numa/duration_time:u/lower_quartile}{0.1639416745}
+\drefset{hdd/emper-io-stealing-pipe-no-comp-numa/duration_time:u/lower_whisker}{0.159135035}
+\drefset{hdd/emper-io-stealing-pipe-no-comp-numa/duration_time:u/upper_whisker}{0.173013421}
+\drefset{hdd/emper-pipe-no-completer/duration_time:u/mean}{0.25473941737999994}
+\drefset{hdd/emper-pipe-no-completer/duration_time:u/std}{0.009243098296085321}
+\drefset{hdd/emper-pipe-no-completer/duration_time:u/min}{0.23616193900000002}
+\drefset{hdd/emper-pipe-no-completer/duration_time:u/max}{0.277770261}
+\drefset{hdd/emper-pipe-no-completer/duration_time:u/median}{0.254373576}
+\drefset{hdd/emper-pipe-no-completer/duration_time:u/upper_quartile}{0.26051450924999997}
+\drefset{hdd/emper-pipe-no-completer/duration_time:u/lower_quartile}{0.24813072149999998}
+\drefset{hdd/emper-pipe-no-completer/duration_time:u/lower_whisker}{0.23616193900000002}
+\drefset{hdd/emper-pipe-no-completer/duration_time:u/upper_whisker}{0.277770261}
+\drefset{hdd/fgrep/duration_time:u/mean}{1.49347303766}
+\drefset{hdd/fgrep/duration_time:u/std}{0.009719072063038936}
+\drefset{hdd/fgrep/duration_time:u/min}{1.4757688489999998}
+\drefset{hdd/fgrep/duration_time:u/max}{1.5209471140000002}
+\drefset{hdd/fgrep/duration_time:u/median}{1.491606681}
+\drefset{hdd/fgrep/duration_time:u/upper_quartile}{1.499134286}
+\drefset{hdd/fgrep/duration_time:u/lower_quartile}{1.4871444187499998}
+\drefset{hdd/fgrep/duration_time:u/lower_whisker}{1.4757688489999998}
+\drefset{hdd/fgrep/duration_time:u/upper_whisker}{1.510669429}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp/duration_time:u/mean}{0.25718139112}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp/duration_time:u/std}{0.009151505246754336}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp/duration_time:u/min}{0.236744215}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp/duration_time:u/max}{0.278181509}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp/duration_time:u/median}{0.256408261}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp/duration_time:u/upper_quartile}{0.264857332}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp/duration_time:u/lower_quartile}{0.25072796575}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp/duration_time:u/lower_whisker}{0.236744215}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp/duration_time:u/upper_whisker}{0.278181509}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-numa/duration_time:u/mean}{0.21157986001999998}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-numa/duration_time:u/std}{0.013580788032137856}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-numa/duration_time:u/min}{0.183987064}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-numa/duration_time:u/max}{0.24415205399999998}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-numa/duration_time:u/median}{0.206337422}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-numa/duration_time:u/upper_quartile}{0.22396209275}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-numa/duration_time:u/lower_quartile}{0.20186353149999997}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-numa/duration_time:u/lower_whisker}{0.183987064}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-numa/duration_time:u/upper_whisker}{0.24415205399999998}
+\drefset{hdd/emper-single-uring-numa/duration_time:u/mean}{5.474185867779999}
+\drefset{hdd/emper-single-uring-numa/duration_time:u/std}{0.21833938230346542}
+\drefset{hdd/emper-single-uring-numa/duration_time:u/min}{5.035003719}
+\drefset{hdd/emper-single-uring-numa/duration_time:u/max}{6.1448526900000005}
+\drefset{hdd/emper-single-uring-numa/duration_time:u/median}{5.4444758360000005}
+\drefset{hdd/emper-single-uring-numa/duration_time:u/upper_quartile}{5.521030329749999}
+\drefset{hdd/emper-single-uring-numa/duration_time:u/lower_quartile}{5.36226457275}
+\drefset{hdd/emper-single-uring-numa/duration_time:u/lower_whisker}{5.192884889000001}
+\drefset{hdd/emper-single-uring-numa/duration_time:u/upper_whisker}{5.6057374929999995}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp-numa/duration_time:u/mean}{0.17012466837999998}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp-numa/duration_time:u/std}{0.004698319715214583}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp-numa/duration_time:u/min}{0.160662189}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp-numa/duration_time:u/max}{0.18086289799999997}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp-numa/duration_time:u/median}{0.16890012699999998}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp-numa/duration_time:u/upper_quartile}{0.172434789}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp-numa/duration_time:u/lower_quartile}{0.16676066675}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp-numa/duration_time:u/lower_whisker}{0.160662189}
+\drefset{hdd/emper-io-notification-waitfd-waitfree-no-comp-numa/duration_time:u/upper_whisker}{0.18086289799999997}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-numa/duration_time:u/mean}{0.22338823391999998}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-numa/duration_time:u/std}{0.01743791800501019}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-numa/duration_time:u/min}{0.193312181}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-numa/duration_time:u/max}{0.273286675}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-numa/duration_time:u/median}{0.2209656345}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-numa/duration_time:u/upper_quartile}{0.2350700425}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-numa/duration_time:u/lower_quartile}{0.21026753199999998}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-numa/duration_time:u/lower_whisker}{0.193312181}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-numa/duration_time:u/upper_whisker}{0.269629321}
+\drefset{hdd/emper-io-stealing-waitfd-no-comp-numa/duration_time:u/mean}{0.16440114953999999}
+\drefset{hdd/emper-io-stealing-waitfd-no-comp-numa/duration_time:u/std}{0.004596092352551839}
+\drefset{hdd/emper-io-stealing-waitfd-no-comp-numa/duration_time:u/min}{0.15745899}
+\drefset{hdd/emper-io-stealing-waitfd-no-comp-numa/duration_time:u/max}{0.17740220499999998}
+\drefset{hdd/emper-io-stealing-waitfd-no-comp-numa/duration_time:u/median}{0.16323825349999999}
+\drefset{hdd/emper-io-stealing-waitfd-no-comp-numa/duration_time:u/upper_quartile}{0.1657778755}
+\drefset{hdd/emper-io-stealing-waitfd-no-comp-numa/duration_time:u/lower_quartile}{0.1615383175}
+\drefset{hdd/emper-io-stealing-waitfd-no-comp-numa/duration_time:u/lower_whisker}{0.15745899}
+\drefset{hdd/emper-io-stealing-waitfd-no-comp-numa/duration_time:u/upper_whisker}{0.170309044}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-no-comp-numa/duration_time:u/mean}{0.16917706172}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-no-comp-numa/duration_time:u/std}{0.005233322699913554}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-no-comp-numa/duration_time:u/min}{0.159555072}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-no-comp-numa/duration_time:u/max}{0.18436214}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-no-comp-numa/duration_time:u/median}{0.1679899975}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-no-comp-numa/duration_time:u/upper_quartile}{0.17131429975}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-no-comp-numa/duration_time:u/lower_quartile}{0.16653703375}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-no-comp-numa/duration_time:u/lower_whisker}{0.159555072}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-no-comp-numa/duration_time:u/upper_whisker}{0.178322762}
+\drefset{hdd/emper-waitfd-no-async/duration_time:u/mean}{0.29848017118000003}
+\drefset{hdd/emper-waitfd-no-async/duration_time:u/std}{0.015636989468969728}
+\drefset{hdd/emper-waitfd-no-async/duration_time:u/min}{0.26727450900000005}
+\drefset{hdd/emper-waitfd-no-async/duration_time:u/max}{0.327005952}
+\drefset{hdd/emper-waitfd-no-async/duration_time:u/median}{0.299189978}
+\drefset{hdd/emper-waitfd-no-async/duration_time:u/upper_quartile}{0.3096573895}
+\drefset{hdd/emper-waitfd-no-async/duration_time:u/lower_quartile}{0.2849618685}
+\drefset{hdd/emper-waitfd-no-async/duration_time:u/lower_whisker}{0.26727450900000005}
+\drefset{hdd/emper-waitfd-no-async/duration_time:u/upper_whisker}{0.327005952}
+\drefset{hdd/go/duration_time:u/mean}{1.8099128576}
+\drefset{hdd/go/duration_time:u/std}{0.11665789185532767}
+\drefset{hdd/go/duration_time:u/min}{1.592069108}
+\drefset{hdd/go/duration_time:u/max}{2.305397466}
+\drefset{hdd/go/duration_time:u/median}{1.8143989275}
+\drefset{hdd/go/duration_time:u/upper_quartile}{1.8597652679999999}
+\drefset{hdd/go/duration_time:u/lower_quartile}{1.76177409375}
+\drefset{hdd/go/duration_time:u/lower_whisker}{1.628154931}
+\drefset{hdd/go/duration_time:u/upper_whisker}{1.9468367450000001}
+\drefset{hdd/emper-vanilla/duration_time:u/mean}{0.30728638826}
+\drefset{hdd/emper-vanilla/duration_time:u/std}{0.017096139415789156}
+\drefset{hdd/emper-vanilla/duration_time:u/min}{0.26805313900000005}
+\drefset{hdd/emper-vanilla/duration_time:u/max}{0.35798413500000004}
+\drefset{hdd/emper-vanilla/duration_time:u/median}{0.30841758099999994}
+\drefset{hdd/emper-vanilla/duration_time:u/upper_quartile}{0.31791277}
+\drefset{hdd/emper-vanilla/duration_time:u/lower_quartile}{0.29650359525000003}
+\drefset{hdd/emper-vanilla/duration_time:u/lower_whisker}{0.26805313900000005}
+\drefset{hdd/emper-vanilla/duration_time:u/upper_whisker}{0.342681059}
+\drefset{hdd/emper-io-stealing-pipe/duration_time:u/mean}{0.29968919169999997}
+\drefset{hdd/emper-io-stealing-pipe/duration_time:u/std}{0.01601791255535804}
+\drefset{hdd/emper-io-stealing-pipe/duration_time:u/min}{0.264012586}
+\drefset{hdd/emper-io-stealing-pipe/duration_time:u/max}{0.345109054}
+\drefset{hdd/emper-io-stealing-pipe/duration_time:u/median}{0.298985276}
+\drefset{hdd/emper-io-stealing-pipe/duration_time:u/upper_quartile}{0.30965440099999997}
+\drefset{hdd/emper-io-stealing-pipe/duration_time:u/lower_quartile}{0.29023688375}
+\drefset{hdd/emper-io-stealing-pipe/duration_time:u/lower_whisker}{0.264012586}
+\drefset{hdd/emper-io-stealing-pipe/duration_time:u/upper_whisker}{0.330563767}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-numa/duration_time:u/mean}{0.21752404188000005}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-numa/duration_time:u/std}{0.01774456797675871}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-numa/duration_time:u/min}{0.19152303599999998}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-numa/duration_time:u/max}{0.267909519}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-numa/duration_time:u/median}{0.214564977}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-numa/duration_time:u/upper_quartile}{0.22349330974999998}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-numa/duration_time:u/lower_quartile}{0.20731638525}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-numa/duration_time:u/lower_whisker}{0.19152303599999998}
+\drefset{hdd/emper-io-notification-waitfree-waitfd-waitfree-numa/duration_time:u/upper_whisker}{0.24047920099999998}
+\drefset{hdd/emper-io-notification-waitfd-numa/duration_time:u/mean}{0.20954308357999998}
+\drefset{hdd/emper-io-notification-waitfd-numa/duration_time:u/std}{0.012427448907075768}
+\drefset{hdd/emper-io-notification-waitfd-numa/duration_time:u/min}{0.185638579}
+\drefset{hdd/emper-io-notification-waitfd-numa/duration_time:u/max}{0.256464513}
+\drefset{hdd/emper-io-notification-waitfd-numa/duration_time:u/median}{0.209249089}
+\drefset{hdd/emper-io-notification-waitfd-numa/duration_time:u/upper_quartile}{0.21614211575}
+\drefset{hdd/emper-io-notification-waitfd-numa/duration_time:u/lower_quartile}{0.201938955}
+\drefset{hdd/emper-io-notification-waitfd-numa/duration_time:u/lower_whisker}{0.185638579}
+\drefset{hdd/emper-io-notification-waitfd-numa/duration_time:u/upper_whisker}{0.23203075099999998}
+\drefset{hdd/ripgrep-j96-numa/duration_time:u/mean}{0.149866707}
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+\drefset{ssd-drop/emper-io-notification-waitfree-waitfd-waitfree/duration_time:u/median}{0.30123095}
+\drefset{ssd-drop/emper-io-notification-waitfree-waitfd-waitfree/duration_time:u/upper_quartile}{0.31432388425}
+\drefset{ssd-drop/emper-io-notification-waitfree-waitfd-waitfree/duration_time:u/lower_quartile}{0.28985931475}
+\drefset{ssd-drop/emper-io-notification-waitfree-waitfd-waitfree/duration_time:u/lower_whisker}{0.26149310600000003}
+\drefset{ssd-drop/emper-io-notification-waitfree-waitfd-waitfree/duration_time:u/upper_whisker}{0.347181191}
+\drefset{disk/nextFibersStolen}{13020.678}
+\drefset{nfs/nextFibersStolen}{51918.04902962207}
+\drefset{disk/ioFibersStolen}{6.3275}
+\drefset{nfs/ioFibersStolen}{22252.3544433095}
+\drefset{disk/avgStealingImprovement}{0.10784613200183818}
+\drefset{nfs/avgStealingImprovement}{1.362138094756921}
+\drefset{avgStealingImprovement}{0.5259434529201991}
diff --git a/fs-eval-results.tex b/fs-eval-results.tex
new file mode 120000
index 0000000000000000000000000000000000000000..3632352e601e98e687f56282f29fc4209daf02cb
--- /dev/null
+++ b/fs-eval-results.tex
@@ -0,0 +1 @@
+data/fs-eval-results.tex
\ No newline at end of file
diff --git a/sections/05-analysis.tex b/sections/05-analysis.tex
index 2852e9bd2ebb6e3d9f461aeb7623c92a78824103..5d203ee52cf68df468cfeb7209f4d0cb5d590b89 100644
--- a/sections/05-analysis.tex
+++ b/sections/05-analysis.tex
@@ -3,7 +3,7 @@
The following section will present and discuss multiple experiments decigned to analyse the novel architectural designs presented in this thesis.
Either micro-benchmarks as well as benchmarks trying to resemble real-world work loads were used.
-The following results were all generated on systems running Linux 5.18 including the \fosswithcommit{\waitfd{}}{https://gitlab.cs.fau.de/i4/manycore/kernel-emper/-/tree/waitfd}{\href{https://gitlab.cs.fau.de/i4/manycore/kernel-emper/-/commit/0e740bca9cfd5b0252e44b96f4d7aa1d69a8bddf}{0e740bca9cfd}} as well as the \fosscontrib{perf rusage}{https://lore.kernel.org/linux-perf-users/20220420102354.468173-1-florian.fischer@muhq.space} patches.
+The following results were all generated on systems running Linux 5.18 including the \fosswithcommit{\waitfd{}}{https://gitlab.cs.fau.de/i4/manycore/kernel-emper/-/tree/waitfd}{\href{https://gitlab.cs.fau.de/i4/manycore/kernel-emper/-/commit/0e740bca9cfd5b0252e44b96f4d7aa1d69a8bddf}{0e740bca9cfd}} as well as the \fosscontrib{perf rusage patches}{https://lore.kernel.org/linux-perf-users/20220420102354.468173-1-florian.fischer@muhq.space}.
\section{Micro Benchmarks}
@@ -279,11 +279,11 @@ The latency difference of \drefcalc{d(iol/emper-io-notification-waitfd-no-comp/t
Last, \cref{fig:io-latency-strategies} presents the latency improvements of the different sleep strategies.
\io{}-based sleep strategies are more responsive for single work items as already presented in \cref{tab:pulse-even} and the specialized \waitfd{} further decreases the latency by combining the efficient task suspension provided by futex by still beeing a suitable \io{} communication object.
-\section{Benchmarks}
+\section{Real World Benchmarks}
In addition to the presented micro-benchmarks, measuring isolated effects, this thesis presents two benchmarks mimicking real word workloads of \cp{} emphasizing \io{}.
-An echo-server experiment evaluated the \cp{}'s network throughput and latency which is relevant for concurrent network service applications and which is one of Go's design goals.
-Additionaly, a simple file search benchmark examines the \cp{}'s file system performance.
+An echo-server experiment evaluates the \cp{}'s network throughput and latency which is relevant for concurrent network service applications (e.g., microservices), which is also one of Go's design goals.
+Additionaly, a simple file search benchmark examines the \cps{} file system performance.
\subsection{Network}
@@ -306,13 +306,17 @@ Constant improvements in \iouring{}\footnote{\href{https://lwn.net/ml/io-uring/2
\subsection{Filesystem}
-To demonstrate the ease of use and the efficiency of the \EMPER{} \io{}-subsystem I wrote a simple file search utility\footnote{\fosscontrib{\EMPER{} fsearch App}{https://gitlab.cs.fau.de/i4/manycore/emper/-/blob/master/apps/fsearch/fsearch.cpp}}.
+To demonstrate the ease of use and the efficiency of the \EMPER{} \io{}-subsystem I wrote a simple \fosscontrib{\EMPER{}-based file search utility}{https://gitlab.cs.fau.de/i4/manycore/emper/-/blob/master/apps/fsearch/fsearch.cpp}}.
The core algorithm without any concurrency is listed in \cref{code:fsearch}.
-This program recursively searches a directory for the occurrence of a fixed string in each file and prints out the file paths containing the searched string.
+The program recursively searches a directory for the occurrence of a fixed string in each file and prints out the file paths containing the searched string.
-Recursively searching files is a great way to analyze the interaction of \EMPER{} and disk \io{} because it is trivially parallelizable and there exist a lot of good well known comparable implementations.
+Recursively searching files is a great way to analyze the interaction of a \cp{} and disk \io{} because it is trivially parallelizable and there exist a lot of good well known comparable implementations.
The following comparison features the probably well known single-threaded fgrep (GNU grep 3.6 provided by debian) as serial baseline, ripgrep, claiming to be the fastest file search utility, as well as a custom Go implementation using comparable code to the \EMPER{} version.
+The \EMPER{} and Go variants spawn a new fiber or goroutine for each encountered directory, which will again spawn a new fiber or goroutine for each encountered regular file.
+The fibers spawned for a regular file will open it, read it in 4K chunks into a stack allocated buffer and searches the data for the substring using the memcmp operation provided by the standard libraries.
+If the search string is encountered they close the file and print the file path.
+
\begin{listing}
\centering
\begin{tcolorbox}[notitle, colback=white, colframe=white]
@@ -335,35 +339,65 @@ The following comparison features the probably well known single-threaded fgrep
\label{fig:fs-teaser}
\end{figure}
-The \EMPER{} and Go variants spawn a new fiber or goroutine for each encountered directory, which will again spawn a new fiber or goroutine for each encountered regular file.
-The fibers spawned for a regular file will open it, read it in 4K chunks into a stack allocated buffer and searches the data for the substring using the memcmp operation provided by the standard libraries.
-If the search string is encountered they close the file and print the file path.
-
As text corpus the source tree of the Linux kernel is used on the tag v5.13.
-Searching a considerably large source code base, as approximation for what user would actually do is common for file search evaluations~\cite{gallant2016ripgrep}.
+Searching a considerably large source code base, as approximation for what users would actually do is common for file search evaluations~\cite{gallant2016ripgrep}.
All search variants are run 50 times on a haystack located on \nfs{}, a HDD, and SSD.
On all the file backend the results are once gathered with warm caches and once after dropping the filesystem caches\footnote{The caches where droped by writing '3' to the file: \texttt{/proc/sys/vm/drop\_caches}~\cite{vanriel2008documentation}.} before each search.
The presented results where generated using the \emper{} \foss{Filesystem Evaluation Artifact}{https://gitlab.cs.fau.de/i4/manycore/emper-fs-eval} on a system featuring a Intel(R) Xeon(R) E7-4830 CPU with 96 cores across 4 \numa{} nodes.
The used disk are a HP MK000960GWEZK (i.e., the SSD) and a HP EH0146FCBVB (i.e., the HDD) connected to the first \numa{} node.
To also reflect on the effects \numa{} potentially has all search variants are run once pinned only to the \numa{} node where the file backend is attached using \texttt{numactl --cpunodebind}.
-When search on \nfs{} the threads are bound to the node containing the network interface, otherwise to the node where the used block device is attached.
+When searching the haystack on \nfs{} the threads are bound to the node containing the network interface, otherwise to the node where the used block device is attached.
+
+Making a fair comparision of file search applications is difficult~\cite{gallant2016ripgrep}, therefore we measured those different storage devices, with or without warm cashes, and potential effects caused by \numa{}.
+\Cref{fig:fs-teaser} shows such an unfair comparison without context favorable for the \cps{}.
+Indeed it is the only configuration (i.e., searching \nfs{}) where the \cps{} have a significant advantage over ripgrep and not actually performing worse.
+
+However, the experiments allow some general observations regardless of the specific variables.
+\begin{itemize}
+
+\item When fairly comparing ripgrep and the simple \cp{} implementations ripgrep wins in all experiments.
+It is not surprising that a highly specialized and optimized utility outperforms naive implementations highlighting the simplicity of the used \cp{} interface.
+
+\item Searching NFS is for most variants considerably slower by roughly a factor of \drefcalc[precision=0]{d(fs/nfs/ripgrep/duration_time:u/median) / d(fs/ssd/ripgrep/duration_time:u/median)} upto a factor of \drefcalc[precision=0]{d(fs/nfs/fgrep/duration_time:u/median) / d(fs/ssd/fgrep/duration_time:u/median)} for the single-threaded fgrep.
+
+\item Using the centralized \EMPER{} \io{} architecture with a single \iouring{} takes on all disk by far the longest time.
+Due to the contention around the central queues the single \iouring{} variant needs on average \fsBothAvgMedians{emper-single-uring} where the next slowest variant the single-threaded fgrep needs \fsBothAvgMedians{fgrep}.
+
+\item The baseline of issuing all \io{} requests using synchronous syscalls instead of submitting them to the workers' rings takes \fsBothAvgMedians{emper-synchronous}.
+It is thus always slower than the \emper{} status quo using a semaphore-based suspension strategy and a \io{}-completer thread, which requires \fsBothAvgMedians{emper-vanilla}.
-Making a fair comparision of file search applications is difficult~\cite{gallant2016ripgrep}, therefore we measured different filesystem backend, with or without warm cashes, and potential effects caused by \numa{}.
-\Cref{fig:fs-teaser} shows such unfair, highly specific comparision favorable for the \cps{}.
-Indead it is the only configuration (i.e., searching \nfs{}) where the \cps{} have a significant advantage over ripgrep and not actually performing worse.
+\item Using an \io{}-based sleep strategies, is beneficial when executing heavy \io{} workloads.
+The status quo performs worse for all experiments except on HDD when dropping the caches it is faster than the pipe strategy.
+Additionally, the sleep strategy using the specialized \waitfd{}-object outperforms the general purpose Linux pipe.
-But first let us explore the effects of the different disk-backed filesystems and caching.
-\Cref{fig:fs-hdd-vs-ssd} shows that the read throughput of the disks as well as the caching is not the limiting factor overall search time.
+\item The \io{}-based sleep strategies, not requiring a completer thread, are in fact only hindered by the completer.
+The variant using a \waitfd{}-based sleep strategy is \fsBothAvgMedianImprovements{emper-waitfd}{emper-waitfd-no-completer} faster without an \io{} completer thread.
+The pipe-based variant performs respectively \fsBothAvgMedianImprovements{emper-pipe}{emper-pipe-no-completer} better.
+
+\item Hardly any \io{}-notifications are posted by the kernel because the runtime is fully utilized during the searching phase, regardless of the used \io{} peripheral.
+
+\item Similarly, only an insignificant number (i.e., \dref{fs/disk/ioFibersStolen} on average) of \io{} completions where stolen during the search on the two disks.
+On contrary when searching on \nfs{} where \io{} requests take longer to complete and the runtime is thus not fully saturated on average \dref{fs/nfs/ioFibersStolen} \io{} completions were stolen.
+Consequently, searching on disk is only negligibly improved on average across all \EMPER{} configurations by \dref{fs/disk/avgStealingImprovement}\% where searching on nfs is improved on average by \io{}-stealing by \dref{fs/nfs/avgStealingImprovement}\%.
+\end{itemize}
+
+But before looking at more specific details first let us explore the effects of the different disk-backed filesystems and caching.
+\Cref{fig:fs-hdd-vs-ssd} shows that the read throughput of the disks as well as the caching is not the limiting factor of the overall search time.
All measured file search applications perform very similar on the four disk and caching variants.
\begin{figure}
\centering
\includegraphics{fs-hdd-vs-ssd}
-\caption{Comparing the results of searching the haystack on different block devices and when dropping the all \os{} caches before searching. The marginal differences suggest that the limiting factor is not the block device or the caching but something unrelated.}
+\caption{Comparing the required times of searching the haystack on different block devices and when dropping all \os{} caches before searching. The marginal differences suggest that the limiting factor is not the block device or the caching but something unrelated.}
\label{fig:fs-hdd-vs-ssd}
\end{figure}
+One difference between the \cp{} applications is that they use all available cores by default, where ripgrep uses only twelve threads per default.
+The thread difference led to the assumption that somewhere must be a highly contended bottleneck.
+Indeed sampling the file search application and the kernel using perf and Brandon Gregg's \cpu{} Flamegraphs\cite{gregg2016flame} reveals that the application spent most of its time serializing itself on the process file table lock during opening and closing files.
+\Cref{fig:fsearch-flamegraph} contains the resulting flamegraph showing that more than 50\% of all samples are collected during the file table spinlock acquisition.
+
\begin{figure}
\includegraphics[width=\textwidth, keepaspectratio]{ssd-waitfd-no-completer-flamegraph}
\caption{Flamegraph showing the recorded samples of a search done on SSD using the \waitfd{} sleep strategy and no completer. The execution is clearly dominated by the synchronization of the process file table due to many parallel open and close requests.}
diff --git a/thesis.tex b/thesis.tex
index 846a5c861a4d08b8fa67bd05cf985fc89150d04a..45da98e04bc240eb06e39558f0411001d7d276cf 100644
--- a/thesis.tex
+++ b/thesis.tex
@@ -59,6 +59,7 @@
\drefinput{syscall-measurements}
\drefinput[sl/]{sleep-latency-eval-results}
\drefinput[iol/]{io-latency-eval-results}
+\drefinput[fs/]{fs-eval-results}
% support referencing footnote
% https://tex.stackexchange.com/questions/10102/multiple-references-to-the-same-footnote-with-hyperref-support-is-there-a-bett