Travelled to:1 × Canada
1 × China
1 × France
1 × India
2 × United Kingdom
7 × USA
Collaborated with:M.Musuvathi A.Diwan K.S.McKinley P.F.Sweeney M.Veanes W.Schulte A.Sampson M.Hauswirth E.H.Chi S.Maleki Y.Ding X.Shen O.Saarikivi V.Raychev J.Bornholt D.Coughlin D.Molnar B.Livshits M.Musuvathi Y.Zhao D.Knights M.C.Mozer P.Panchekha D.Grossman L.Ceze B.Ren G.Agrawal J.R.Larus T.Poutanen A.Kansal T.S.Saponas A.J.B.Brush R.Ziola D.Garbervetsky Z.Pavlinovic M.B.0001 E.Zoppi R.Dathathri Hao Chen 0030 Kim Laine Kristin E. Lauter
Talks about:data (6) parallel (5) optim (4) program (3) uncertain (2) accuraci (2) static (2) profil (2) effici (2) wrong (2)
Person: Todd Mytkowicz
DBLP: Mytkowicz:Todd
Contributed to:
2015
2015
2015
2015
2014
2014
2014
2013
201320102009
20092009
2008
2017201720172019Wrote 19 papers:
- ICML-2015-DingZSMM #consistency
- Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup (YD, YZ, XS, MM, TM), pp. 579–587.
- POPL-2015-VeanesMML #source code #string
- Data-Parallel String-Manipulating Programs (MV, TM, DM, BL), pp. 139–152.
- SOSP-2015-RaychevMM #execution #symbolic computation #using
- Parallelizing user-defined aggregations using symbolic execution (VR, MM, TM), pp. 153–167.
- VLDB-2015-DingSMM #algorithm #framework #named #optimisation #problem
- TOP: A Framework for Enabling Algorithmic Optimizations for Distance-Related Problems (YD, XS, MM, TM), pp. 1046–1057.
- ASPLOS-2014-BornholtMM #first-order #named #nondeterminism
- Uncertain: a first-order type for uncertain data (JB, TM, KSM), pp. 51–66.
- ASPLOS-2014-MytkowiczMS #finite #state machine
- Data-parallel finite-state machines (TM, MM, WS), pp. 529–542.
- PLDI-2014-SampsonPMMGC #probability #verification
- Expressing and verifying probabilistic assertions (AS, PP, TM, KSM, DG, LC), p. 14.
- PPoPP-2014-MalekiMM #convergence #programming #rank
- Parallelizing dynamic programming through rank convergence (SM, MM, TM), pp. 219–232.
- CGO-2013-RenALMPS #data type #parallel
- SIMD parallelization of applications that traverse irregular data structures (BR, GA, JRL, TM, TP, WS), p. 10.
- OOPSLA-2013-KansalSBMMZ #abstraction #energy #latency #mobile #performance
- The latency, accuracy, and battery (LAB) abstraction: programmer productivity and energy efficiency for continuous mobile context sensing (AK, TSS, AJBB, KSM, TM, RZ), pp. 661–676.
- PLDI-2010-MytkowiczDHS #java
- Evaluating the accuracy of Java profilers (TM, AD, MH, PFS), pp. 187–197.
- ASPLOS-2009-MytkowiczDHS #exclamation
- Producing wrong data without doing anything obviously wrong! (TM, AD, MH, PFS), pp. 265–276.
- CC-2009-KnightsMSMD #hardware #optimisation
- Blind Optimization for Exploiting Hardware Features (DK, TM, PFS, MCM, AD), pp. 251–265.
- OOPSLA-2009-MytkowiczCD #profiling
- Inferred call path profiling (TM, DC, AD), pp. 175–190.
- HT-2008-ChiM #comprehension #performance #social #using
- Understanding the efficiency of social tagging systems using information theory (EHC, TM), pp. 81–88.
- ESEC-FSE-2017-GarbervetskyP0M #big data #optimisation #query #static analysis
- Static analysis for optimizing big data queries (DG, ZP, MB0, MM, TM, EZ), pp. 932–937.
- OOPSLA-2017-SampsonMM #programming
- Static stages for heterogeneous programming (AS, KSM, TM), p. 27.
- PLDI-2017-SaarikiviVMM
- Fusing effectful comprehensions (OS, MV, TM, MM), pp. 17–32.
- PLDI-2019-DathathriS0LLMM #compilation #named #optimisation
- CHET: an optimizing compiler for fully-homomorphic neural-network inferencing (RD, OS, HC0, KL, KEL, SM, MM, TM), pp. 142–156.