Travelled to:1 × Austria
1 × Canada
1 × China
1 × Hungary
1 × Portugal
1 × Sweden
13 × USA
2 × France
Collaborated with:∅ G.Singh M.T.Vechev P.A.Milder F.Franchetti J.C.Hoe Y.Voronenko T.Rompf D.G.Spampinato M.Zuluaga F.d.Mesmay G.Ofenbeck A.Stojanov A.Krause N.Kyrtatas B.Hess T.R.Gross S.Chellappa P.Tummeltshammer G.Sergent A.Rimmel G.Nordin C.F.Fang R.A.Rutenbar T.Chen T.Gehr M.Odersky
Talks about:generat (7) fast (6) program (5) numer (5) transform (4) perform (4) librari (4) automat (4) compil (4) abstract (3)
♂ Person: Markus Püschel
DBLP: P=uuml=schel:Markus
Facilitated 2 volumes:
SciCoSciCoContributed to:
2015
2015
2014
20142013
2013
2012
2012
2012
2011200920092009
20082008200720072005200520042003
201620172019
2018
201720182019Wrote 28 papers:
- DATE-2015-KyrtatasSP #algebra #compilation #embedded #linear
- A basic linear algebra compiler for embedded processors (NK, DGS, MP), pp. 1054–1059.
- PLDI-2015-SinghPV #performance #program analysis
- Making numerical program analysis fast (GS, MP, MTV), pp. 303–313.
- CGO-2014-SpampinatoP #algebra #compilation #linear
- A Basic Linear Algebra Compiler (DGS, MP), p. 23.
- GPCE-2014-HessGP #automation #interface
- Automatic locality-friendly interface extension of numerical functions (BH, TRG, MP), pp. 83–92.
- GPCE-2013-OfenbeckRSOP #generative #library #performance #scala #towards
- Spiral in scala: towards the systematic construction of generators for performance libraries (GO, TR, AS, MO, MP), pp. 125–134.
- ICML-c1-2013-ZuluagaSKP #learning #multi #optimisation
- Active Learning for Multi-Objective Optimization (MZ, GS, AK, MP), pp. 462–470.
- DAC-2012-ZuluagaMP #generative #network #sorting #streaming
- Computer generation of streaming sorting networks (MZ, PAM, MP), pp. 1245–1253.
- LCTES-2012-ZuluagaKMP #design #predict
- “Smart” design space sampling to predict Pareto-optimal solutions (MZ, AK, PAM, MP), pp. 119–128.
- PEPM-2012-Puschel #compilation #performance
- Compiling math to fast code (MP), pp. 1–2.
- Onward-2011-Puschel #automation #performance #programming
- Automatic performance programming (MP), pp. 1–2.
- CGO-2009-VoronenkoMP #generative #library #linear
- Computer Generation of General Size Linear Transform Libraries (YV, FdM, MP), pp. 102–113.
- DATE-2009-MilderHP #automation #generative #permutation #streaming
- Automatic generation of streaming datapaths for arbitrary fixed permutations (PAM, JCH, MP), pp. 1118–1123.
- ICML-2009-MesmayRVP #graph #library #optimisation #performance
- Bandit-based optimization on graphs with application to library performance tuning (FdM, AR, YV, MP), pp. 729–736.
- CC-2008-FranchettiP #generative #permutation
- Generating SIMD Vectorized Permutations (FF, MP), pp. 116–131.
- DAC-2008-MilderFHP #implementation #representation
- Formal datapath representation and manipulation for implementing DSP transforms (PAM, FF, JCH, MP), pp. 385–390.
- GPCE-2007-Puschel #education #library #performance #question
- Can we teach computers to write fast libraries? (MP), pp. 1–2.
- GTTSE-2007-ChellappaFP #how #performance
- How to Write Fast Numerical Code: A Small Introduction (SC, FF, MP), pp. 196–259.
- DAC-2005-NordinMHP #automation #fourier #generative
- Automatic generation of customized discrete fourier transform IPs (GN, PAM, JCH, MP), pp. 471–474.
- PLDI-2005-FranchettiVP
- Formal loop merging for signal transforms (FF, YV, MP), pp. 315–326.
- DAC-2004-TummeltshammerHP #constant #multi
- Multiple constant multiplication by time-multiplexed mapping of addition chains (PT, JCH, MP), pp. 826–829.
- DAC-2003-FangRPC #modelling #performance #static analysis #towards
- Toward efficient static analysis of finite-precision effects in DSP applications via affine arithmetic modeling (CFF, RAR, MP, TC), pp. 496–501.
- ASE-2016-Puschel #generative #performance
- Program generation for performance (MP), p. 1.
- GPCE-2017-OfenbeckRP #programming #staging
- Staging for generic programming in space and time (GO, TR, MP), pp. 15–28.
- GPCE-2019-StojanovRP #compilation #information retrieval
- A stage-polymorphic IR for compiling MATLAB-style dynamic tensor expressions (AS, TR, MP), pp. 34–47.
- CAV-2018-SinghPV #learning #performance #program analysis
- Fast Numerical Program Analysis with Reinforcement Learning (GS, MP, MTV), pp. 211–229.
- POPL-2017-SinghPV #abstract domain #performance
- Fast polyhedra abstract domain (GS, MP, MTV), pp. 46–59.
- POPL-2018-SinghPV #abstract domain
- A practical construction for decomposing numerical abstract domains (GS, MP, MTV), p. 28.
- POPL-2019-SinghGPV #abstract domain #network
- An abstract domain for certifying neural networks (GS, TG, MP, MTV), p. 30.