The tao of parallelism in algorithms
By Keshav Pingali, Donald Nguyen, Milind Kulkarni, Martin Burtscher, M. Hassaan, R. Kaleem, Tsung-Hsien Lee, Andrew Lenharth, R. Manevich, Mario Méndez-Lojo and 2 more. ACM-SIGPLAN Symposium on Programming Language Design and Implementation, 2011
436 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Hardware and Architecture
View Paper: https://www.semanticscholar.org/paper/c0aac0d7b4b3ddc352f77d8fafc6419e67a0c729
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Nadathur Rajagopalan Satish
- Pradeep K. Dubey
- Jongsoo Park
- John Douglas Owens
- Keshav Pingali
- Vipin Kumar
- Charu C. Aggarwal
In Hardware and Architecture
- Nadathur Rajagopalan Satish
- Pradeep K. Dubey
- Jongsoo Park
- John Douglas Owens
- Keshav Pingali
- Vipin Kumar
Citations in the corpus
- AvA: Accelerated Virtualization of Accelerators (2020, neutral): “7 19 444 676 QATzip Intel QAT 8970 HIP 41 624 990 Galois [75] AMD Vega 64”
- Gunrock: a high-performance graph processing library on the GPU (2015, background): “…graph libraries [20]; PowerGraph, a popular distributed graph library [12]; and Ligra, one of the highest-performing multi-core shared-memory graph libraries [28] (Galois [23, 25] would have also been a good choice here; Galois and Ligra represent the best-performing CPU implementations).”
- A lightweight infrastructure for graph analytics (2013, methodology): “To discuss common issues in graph analytics, it is convenient to use the terminology of amorphous dataparallelism (ADP) [26], a data-centric programming model for expressing parallelism in regular and irregular algorithms.”
- GraphMat: High performance graph analytics made productive (2015, background): “…recent work [28] that has compared different graph frameworks including Giraph [1] and GraphLab [5, 22] which are two popular vertex programming models; CombBLAS [3, 11], a matrix programming model; Socialite [29], a functional programming model; and Galois [26, 4, 25], a task-based abstraction.”
- Navigating the maze of graph analytics frameworks using massive graph datasets (2014, methodology): “The algorithm works as follows: Each vertex shares its neighborhood list with each of its neighbors.”
- Trends in big data analytics (2014, background): “Speculative-parallelism (or amorphous data-parallelism) [79] is where parallel execution of tasks can lead to potentially conflicting concurrent computations.”