PPT-Can PRAM Graph Algorithms Provide Practical Speedups on Man

George Caragea and Uzi Vishkin University of Maryland 1 Speaker James Edwards It has proven to be quite difficult to obtain significant performance improvements using current parallel computing platforms. Sathish. . Vadhiyar. PRAM Model - Introduction. Parallel Random Access Machine. Allows parallel-algorithm designers to treat processing power as unlimited. Ignores complexity of inter-process communication. B. Gibbons Computer Science Division University of California Berkeley, CA 94720 paper introduces the Asynchronous PRAM model of computation, a variant of the PRAM in which the processors run asy ~ch 1. Graph Algorithms. Many problems are naturally represented as graphs. Networks, Maps, Possible paths, Resource Flow, etc.. Ch. 3 focuses on algorithms to find connectivity in graphs. Ch. 4 focuses on algorithms to find paths within graphs. L. á. szl. ó. . Lov. á. sz. . Eötvös. . Lor. ánd. . University, Budapest . May 2013. 1. Happy Birthday Ravi!. May 2013. 2. Cut norm . of . matrix A. . . n. x. n. :. The Weak Regularity Lemma. Time for Hardware Upgrade. Uzi Vishkin. . . The pompous version. After 40 years of “wandering in the desert”, general-purpose parallelism is very close to capturing the “promised land” of mainstream computing. matter and can matter more: . Big . lead of PRAM algorithms on prototype-HW. Uzi Vishkin. There is nothing more practical than a good theory. -- James C. Maxwell. Mainstream many-cores are in trouble. Tekin. . Bicer. , . Jian. Yin, David Chiu, . Gagan. . Agrawal. . and Karen . Schuchardt. Ohio State University. Washington State University. Pacific Northwest National Laboratories. 1. †. †. Sathish. . Vadhiyar. PRAM Model - Introduction. Parallel Random Access Machine. Allows parallel-algorithm designers to treat processing power as unlimited. Ignores complexity of inter-process communication. R. Garcia is supported by an NSF Bridge to the Doctorate Fellowships. .. The biological imaging group is supported by MH-086994, NSF-1039620, and NSF-0964114.. . Abstract. Automating segmentation of individual neurons in electron microscopic (EM) images is a crucial step in the acquisition and analysis of connectomes. It is commonly thought that approaches which use contextual information from distant parts of the image to make local decisions, should be computationally infeasible. Combined with the topological complexity of three-dimensional (3D) space, this belief has been deterring the development of algorithms that work genuinely in 3D. . 10 Bat Algorithms Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier, 2014 The bat algorithm (BA) is a bio-inspired algorithm developed by Xin-She Yang in 2010. 10.1 Echolocation of Bats Matching Algorithms and Networks Algorithms and Networks: Matching 2 This lecture Matching: problem statement and applications Bipartite matching (recap) Matching in arbitrary undirected graphs: Edmonds algorithm Lecture 17: Shortest Paths. Catie Baker. Spring . 2015. Announcements. Homework . 4 due next Wednesday, May . 13th. Spring 2015. 2. CSE373: Data Structures & Algorithms. Graph Traversals. For an arbitrary graph and a starting node . Lecture . 17: More . Dijkstra. ’s. and. Minimum Spanning Trees. Aaron Bauer. Winter 2014. Dijkstra’s. Algorithm: Idea. Winter 2014. 2. CSE373: Data Structures & Algorithms. Initially, start node has cost 0 and all other nodes have cost . Contrt NoGREEMET ismaded into thiso 20NTYOS AES fter Counfter 1 Nu whiis at lst 200 miles outsthephicl bouies ofos Ales CCompltion ofthe povisions of this Ament nt of the rlocion incntiveshall be ont