G
George Almási
Researcher at IBM
Publications - 58
Citations - 2661
George Almási is an academic researcher from IBM. The author has contributed to research in topics: Supercomputer & Cellular architecture. The author has an hindex of 26, co-authored 58 publications receiving 2622 citations. Previous affiliations of George Almási include University of Illinois at Urbana–Champaign.
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Proceedings ArticleDOI
An Overview of the BlueGene/L Supercomputer
N. R. Adiga,Gheorghe Almasi,George Almási,Y. Aridor,Rajkishore Barik,D. Beece,Ralph Bellofatto,Gyan Bhanot,R. Bickford,Matthias A. Blumrich,A. A. Bright,Jose R. Brunheroto,Calin Cascaval,José G. Castaños,Waiman Chan,Luis Ceze,Paul W. Coteus,Siddhartha Chatterjee,Dong Chen,G. Chiu,Thomas Mario Cipolla,Paul G. Crumley,K.M. Desai,A. Deutsch,T. Domany,M. B. Dombrowa,Wilm E. Donath,Maria Eleftheriou,C. Christopher Erway,J. Esch,Blake G. Fitch,J. Gagliano,Alan Gara,Rahul Garg,Robert S. Germain,Mark E. Giampapa,B. Gopalsamy,John A. Gunnels,Manish Gupta,Fred G. Gustavson,Shawn A. Hall,R. A. Haring,D. Heidel,P. Heidelberger,L.M. Herger,Dirk Hoenicke,Rory D. Jackson,T. Jamal-Eddine,Gerard V. Kopcsay,Elie Krevat,Manish P. Kurhekar,A.P. Lanzetta,Derek Lieber,L.K. Liu,M. Lu,M. Mendell,A. Misra,Yosef Moatti,L. Mok,José E. Moreira,Ben J. Nathanson,M. Newton,Martin Ohmacht,Adam J. Oliner,Vinayaka Pandit,R.B. Pudota,Rick A. Rand,R. Regan,B. Rubin,Albert E. Ruehli,Silvius Rus,Ramendra K. Sahoo,A. Sanomiya,Eugen Schenfeld,M. Sharma,E. Shmueli,Suryabhan Singh,Peilin Song,Vijayalakshmi Srinivasan,Burkhard Steinmacher-Burow,Karin Strauss,C. Surovic,Richard A. Swetz,Todd E. Takken,R.B. Tremaine,M. Tsao,A. R. Umamaheshwaran,P. Verma,Pavlos M. Vranas,T.J.C. Ward,M. Wazlowski,William A. Barrett,C. Engel,B. Drehmel,B. Hilgart,D. Hill,F. Kasemkhani,D. Krolak,C.T. Li,T. Liebsch,James Anthony Marcella,Adam J. Muff,A. Okomo,M. Rouse,A. Schram,Matthew R. Tubbs,G. Ulsh,Charles D. Wait,J. Wittrup,M. Bae,Kenneth Alan Dockser,Lynn Kissel,M.K. Seager,Jeffrey S. Vetter,K. Yates +114 more
TL;DR: An overview of the BlueGene/L Supercomputer, a massively parallel system of 65,536 nodes based on a new architecture that exploits system-on-a-chip technology to deliver target peak processing power of 360 teraFLOPS (trillion floating-point operations per second).
Journal ArticleDOI
Blue Gene: a vision for protein science using a petaflop supercomputer
F. E. Allen,George Almási,Wanda Andreoni,D. Beece,Bruce J. Berne,A. A. Bright,Jose R. Brunheroto,Calin Cascaval,José G. Castaños,Paul W. Coteus,Paul G. Crumley,Alessandro Curioni,Monty M. Denneau,Wilm E. Donath,Maria Eleftheriou,Blake G. Fitch,Bruce M. Fleischer,Christos John Georgiou,Robert S. Germain,Mark E. Giampapa,Donna L. Gresh,Manish Gupta,R. A. Haring,H. Ho,Peter H. Hochschild,Susan Flynn Hummel,T. Jonas,Derek Lieber,Glenn J. Martyna,K. Maturu,José E. Moreira,D.M. Newns,M. Newton,Robert Alan Philhower,T. Picunko,Jed W. Pitera,Michael C. Pitman,Rick A. Rand,Ajay K. Royyuru,Valentina Salapura,A. Sanomiya,R. Shah,Yuk Y. Sham,Suryabhan Singh,Marc Snir,Frank Suits,Richard A. Swetz,William C. Swope,N. Vishnumurthy,T.J.C. Ward,Henry S. Warren,Ruhong Zhou +51 more
TL;DR: An overview of the Blue Gene project at IBM Research is provided to advance the understanding of the mechanisms behind protein folding via large-scale simulation, and to explore novel ideas in massively parallel machine architecture and software.
Journal ArticleDOI
Personalization of Supermarket Product Recommendations
TL;DR: A personalized recommender system designed to suggest new products to supermarket shoppers in a pervasive computing environment in which supermarket customers use Personal Digital Assistants to compose and transmit their orders to the store, which assembles them for subsequent pickup.
Proceedings ArticleDOI
Optimization of MPI collective communication on BlueGene/L systems
George Almási,Philip Heidelberger,Charles J. Archer,Xavier Martorell,C. Christopher Erway,José E. Moreira,Burkhard Steinmacher-Burow,Yili Zheng +7 more
TL;DR: This paper discusses the implementation of machine-optimized MPI collectives on BlueGene/L, describing the algorithms and presenting performance results measured with targeted micro-benchmarks on real Blue Gene/L hardware with up to 4096 compute nodes.
Journal ArticleDOI
Calculating stack distances efficiently
TL;DR: By using a new data structure and various optimization techniques, instrumented run-times within 50 to 100 times the original optimized run- times of the authors' benchmarks are obtained.