J
Jesse Fang
Researcher at Intel
Publications - 26
Citations - 863
Jesse Fang is an academic researcher from Intel. The author has contributed to research in topics: Cache & Profiling (computer programming). The author has an hindex of 17, co-authored 26 publications receiving 846 citations.
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Patent
Shared virtual memory
Hu Chen,Ying Gao,Zhou Xiaocheng,Shoumeng Yan,Peinan Zhang,Mohan Rajagopalan,Jesse Fang,Avi Mendelson,Bratin Saha +8 more
TL;DR: In this paper, the authors present a programming model for CPU-GPU platforms, which allows software vendors to write a single application stack and target it to all the different platforms, and a shared memory model between the CPU and GPU.
Proceedings ArticleDOI
Enabling scalability and performance in a large scale CMP environment
Bratin Saha,Ali-Reza Adl-Tabatabai,Anwar Ghuloum,Mohan Rajagopalan,Richard L. Hudson,Leaf Petersen,Vijay Menon,Brian R. Murphy,Tatiana Shpeisman,Eric Sprangle,Anwar Rohillah,Doug Carmean,Jesse Fang +12 more
TL;DR: This paper presents the architecture of McRT and discusses the experiences with the system, including experimental evaluation that lead to several interesting, non-intuitive findings, providing key insights about the structure of the system stack at this scale.
Proceedings ArticleDOI
Programming model for a heterogeneous x86 platform
Bratin Saha,Xiaocheng Zhou,Hu Chen,Ying Gao,Shoumeng Yan,Mohan Rajagopalan,Jesse Fang,Peinan Zhang,Ronny Ronen,Avi Mendelson +9 more
TL;DR: A programming model for such heterogeneous platforms consisting of a combination of cores focused on scalar performance, and a set of throughput-oriented cores is described.
Proceedings ArticleDOI
Path profile guided partial redundancy elimination using speculation
TL;DR: The authors present a path profile guided partial redundancy elimination algorithm that uses speculation to enable the removal of redundancy along more frequently executed paths at the expense of introducing additional expression evaluations along less frequently executedpaths.
Patent
Optimizing code by exploiting speculation and predication with a cost-benefit data flow analysis based on path profiling information
TL;DR: In this paper, a method and apparatus for optimizing execution of code is disclosed, where the code is executed to generate path profiling information and a cost and a benefit are calculated for relocating the at least one of the plurality of instructions to the least one location, the cost and the benefit based on the path profiling.