C
Charles D. Wait
Researcher at IBM
Publications - 34
Citations - 1266
Charles D. Wait is an academic researcher from IBM. The author has contributed to research in topics: Addressing mode & Instruction register. The author has an hindex of 13, co-authored 34 publications receiving 1259 citations.
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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).
Patent
Multi-petascale highly efficient parallel supercomputer
Sameh W. Asaad,Ralph Bellofatto,Michael A. Blocksome,Matthias A. Blumrich,Peter Boyle,Jose R. Brunheroto,Dong Chen,Chen-Yong Cher,George Liang-Tai Chiu,N. H. Christ,Paul W. Coteus,Kristan D. Davis,Gabor Dozsa,Alexandre E. Eichenberger,Noel A. Eisley,Matthew R. Ellavsky,Kahn C. Evans,Bruce M. Fleischer,Thomas W. Fox,Alan Gara,Mark E. Giampapa,Thomas M. Gooding,Michael K. Gschwind,John A. Gunnels,Shawn A. Hall,Rudolf A. Haring,Philip Heidelberger,Todd A. Inglett,Brant L. Knudson,Gerard V. Kopcsay,Sameer Kumar,Amith R. Mamidala,James Anthony Marcella,Mark G. Megerian,Douglas R. Miller,Samuel J. Miller,Adam J. Muff,Michael B. Mundy,John Kevin Patrick O'Brien,Kathryn M. O'Brien,Martin Ohmacht,Jeffrey J. Parker,Ruth J. Poole,Joseph D. Ratterman,Valentina Salapura,David L. Satterfield,Robert M. Senger,Brian E. Smith,Burkhard Steinmacher-Burow,William Maupin Stockdell,Craig B. Stunkel,Krishnan Sugavanam,Yutaka Sugawara,Todd E. Takken,Barry M. Trager,James L. Van Oosten,Charles D. Wait,Robert E. Walkup,Alfred T. Watson,Robert W. Wisniewski,Peng Wu +60 more
TL;DR: A multi-petascale Highly Efficient Parallel Supercomputer of 100 petaOPS-scale computing, at decreased cost, power and footprint, allows for a maximum packaging density of processing nodes from an interconnect point of view.
Patent
Structural Power Reduction in Multithreaded Processor
TL;DR: In this article, a circuit arrangement and method utilize a plurality of execution units having different power and performance characteristics and capabilities within a multithreaded processor core, and selectively route instructions having different performance requirements to different execution units.
Patent
Floating point execution unit with fixed point functionality
TL;DR: A floating point execution unit is capable of selectively repurposing one or more adders in an exponent path of the floating point unit to perform fixed point addition operations, thereby providing fixed point functionality in the floating-point execution unit as mentioned in this paper.
Proceedings ArticleDOI
A High-Performance SIMD Floating Point Unit for BlueGene/L: Architecture, Compilation, and Algorithm Design
Leonardo Bachega,Siddhartha Chatterjee,Kenneth Alan Dockser,John A. Gunnels,Manish Gupta,Fred G. Gustavson,Christopher A. Lapkowski,Gary K. Liu,M. Mendell,Charles D. Wait,T. J. Chris Ward +10 more
TL;DR: Preliminary performance data shows that the algorithm-compiler-hardware combination delivers a significant fraction of peak floating-point performance for compute-bound kernels such as matrix multiplication, and delivery of peak memory bandwidth for memory-bound kernel such as daxpy, while being largely insensitive to data alignment.