Travelled to:1 × Austria
1 × China
1 × Germany
1 × New Zealand
1 × Sweden
1 × United Kingdom
2 × Canada
9 × USA
Collaborated with:A.Diwan D.Zaparanuks P.F.Sweeney A.Adamoli D.Makarov L.Mastrangelo N.Nystrom T.Mytkowicz T.M.Chilimbi M.Vitásek W.Binder M.Jovic M.Burtscher M.C.Mozer M.Hind A.B.d.Oliveira S.Fischmeister L.Ponzanelli A.Mocci M.Lanza D.F.Bacon P.Cheng D.Frampton D.Grove V.T.Rajan D.Clarke T.Clear K.Fisler S.Krishnamurthi J.G.Politz V.Tirronen T.Wrigstad
Talks about:profil (5) java (4) data (3) use (3) algorithm (2) perform (2) analysi (2) visual (2) vertic (2) shadow (2)
Person: Matthias Hauswirth
DBLP: Hauswirth:Matthias
Contributed to:
2015
2015
2014
2013
2013
2012
2011
201120112010
20102009
200620052004200420022019Wrote 18 papers:
- GPCE-2015-MakarovH #compilation #multi #named
- CLOP: a multi-stage compiler to seamlessly embed heterogeneous code (DM, MH), pp. 109–112.
- OOPSLA-2015-MastrangeloPMLH #api #java
- Use at your own risk: the Java unsafe API in the wild (LM, LP, AM, ML, MH, NN), pp. 695–710.
- ITiCSE-WGR-2014-ClarkeCFHKPTW #bibliography #perspective
- In-Flow Peer Review (DC, TC, KF, MH, SK, JGP, VT, TW), pp. 59–79.
- ASPLOS-2013-OliveiraFDHS #why
- Why you should care about quantile regression (ABdO, SF, AD, MH, PFS), pp. 207–218.
- PASTE-2013-VitasekBH #java #named
- ShadowData: shadowing heap objects in Java (MV, WB, MH), pp. 17–24.
- PLDI-2012-ZaparanuksH #algorithm #profiling
- Algorithmic profiling (DZ, MH), pp. 67–76.
- ECOOP-2011-ZaparanuksH #algorithm #design
- The Beauty and the Beast: Separating Design from Algorithm (DZ, MH), pp. 27–51.
- MoDELS-2011-ZaparanuksH #modelling
- Vision Paper: The Essence of Structural Models (DZ, MH), pp. 470–479.
- OOPSLA-2011-JovicAH #debugging #detection #performance
- Catch me if you can: performance bug detection in the wild (MJ, AA, MH), pp. 155–170.
- PLDI-2010-MytkowiczDHS #java
- Evaluating the accuracy of Java profilers (TM, AD, MH, PFS), pp. 187–197.
- SOFTVIS-2010-AdamoliH #analysis #framework #named #performance #visualisation
- Trevis: a context tree visualization & analysis framework and its use for classifying performance failure reports (AA, MH), pp. 73–82.
- ASPLOS-2009-MytkowiczDHS #exclamation
- Producing wrong data without doing anything obviously wrong! (TM, AD, MH, PFS), pp. 265–276.
- CC-2006-BaconCFGHR #analysis #named #online #realtime #visualisation
- Demonstration: On-Line Visualization and Analysis of Real-Time Systems with TuningFork (DFB, PC, DF, DG, MH, VTR), pp. 96–100.
- OOPSLA-2005-HauswirthDSM #automation #profiling
- Automating vertical profiling (MH, AD, PFS, MCM), pp. 281–296.
- ASPLOS-2004-HauswirthC #adaptation #detection #memory management #profiling #statistics #using
- Low-overhead memory leak detection using adaptive statistical profiling (MH, TMC), pp. 156–164.
- OOPSLA-2004-HauswirthSDH #behaviour #comprehension #profiling
- Vertical profiling: understanding the behavior of object-priented applications (MH, PFS, AD, MH), pp. 251–269.
- PLDI-2002-BurtscherDH #classification #predict
- Static Load Classification for Improving the Value Predictability of Data-Cache Misses (MB, AD, MH), pp. 222–233.
- OOPSLA-2019-MastrangeloHN #empirical #java #source code
- Casting about in the dark: an empirical study of cast operations in Java programs (LM, MH, NN), p. 31.