Bill Dally

Dally has received a Bachelor's degree in electrical engineering from Virginia Tech.[6] While working for Bell Telephone Laboratories he contributed to the design of the Bellmac 32, an early 32-bit microprocessor, and earned an Master's degree in electrical engineering from Stanford University in 1981. He then went to the California Institute of Technology (Caltech) from 1983 to 1986,[6] graduating with a Ph.D. degree in computer science in 1986. At Caltech he designed the MOSSIM simulation engine and an integrated circuit for routing. While at Caltech, he was part of the founding group of Stac Electronics in 1983.[7]

From 1986 to 1997 he taught at MIT where he and his group built the J–Machine and the M–Machine,[8] parallel machines emphasizing low overhead synchronization and communication. During his MIT times he claims to have collaborated on developing design of Cray T3D and Cray T3E supercomputers. He became the Willard R. and Inez Kerr Bell Professor in the Stanford University School of Engineering and chairman of the computer science department at Stanford. He served as chairman for twelve years before moving on to Nvidia.[9]

Dally's corporate involvements include various collaborations at Cray Research since 1989. He did Internet router work at Avici Systems starting in 1997, was chief technical officer at Velio Communications from 1999 until its 2003 acquisition by LSI Logic, founder and chairman of Stream Processors, Inc until it folded.[7]

Dally was elected a Fellow of the Association for Computing Machinery in 2002, and a Fellow of the IEEE, also in 2002. In 2003 he became a consultant for NVIDIA for the first time and helped to develop GeForce 8800 GPUs series.[10] He received the ACM/SIGARCH Maurice Wilkes Award in 2000, the Seymour Cray Computer Science and Engineering Award in 2004, and the IEEE Computer Society Charles Babbage Award in 2006. In 2007 he was elected to the American Academy of Arts and Sciences.

In January 2009 he was appointed chief scientist of Nvidia.[11] He worked full-time at Nvidia, while supervising about 12 of his graduate students at Stanford.[12] He is currently chief scientist and SVP of Nvidia Research.[13]

In 2009, he was elected to the National Academy of Engineering for contributions to the design of high-performance interconnect networks and parallel computer architectures.

He received the 2010 ACM/IEEE Eckert–Mauchly Award for "outstanding contributions to the architecture of interconnection networks and parallel computers."[14]