Update propagation in distributed memory hierarchy

2002 ◽  
Author(s):  
M. Bellew ◽  
M. Hsu ◽  
V.-O. Tam
Keyword(s):  
Distributed Memory ◽  
Memory Hierarchy ◽  
Update Propagation

scholarly journals A Distributed Memory Hierarchy and Data Management for Interactive Scene Navigation and Modification on Tiled Display Walls

2015 ◽  
Vol 21 (6) ◽  
pp. 714-729 ◽  
Author(s):  
Duy-Quoc Lai ◽  
Behzad Sajadi ◽  
Shan Jiang ◽  
Gopi Meenakshisundaram ◽  
Aditi Majumder
Keyword(s):  
Data Management ◽  
Distributed Memory ◽  
Memory Hierarchy ◽  
Tiled Display ◽  
Display Walls

scholarly journals MILC Code Performance on High End CPU and GPU Supercomputer Clusters

2018 ◽  
Vol 175 ◽  
pp. 02009
Author(s):  
Carleton DeTar ◽  
Steven Gottlieb ◽  
Ruizi Li ◽  
Doug Toussaint
Keyword(s):  
Conjugate Gradient ◽  
Memory Hierarchy ◽  
Xeon Phi ◽  
Intel Xeon Phi ◽  
Code Performance ◽  
Recent Developments ◽  
Knights Landing ◽  
Many Core ◽  
Intel Xeon

With recent developments in parallel supercomputing architecture, many core, multi-core, and GPU processors are now commonplace, resulting in more levels of parallelism, memory hierarchy, and programming complexity. It has been necessary to adapt the MILC code to these new processors starting with NVIDIA GPUs, and more recently, the Intel Xeon Phi processors. We report on our efforts to port and optimize our code for the Intel Knights Landing architecture. We consider performance of the MILC code with MPI and OpenMP, and optimizations with QOPQDP and QPhiX. For the latter approach, we concentrate on the staggered conjugate gradient and gauge force. We also consider performance on recent NVIDIA GPUs using the QUDA library.

Implementing actor-based primitives on distributed-memory architectures

1991 ◽  
Vol 2 (2) ◽  
pp. 45-49 ◽  
Author(s):  
Michele Di Santo ◽  
Giulio Iannello
Keyword(s):  
Distributed Memory ◽  
Memory Architectures

Cache and Memory Hierarchy Design

1995 ◽  
Vol 23 (3) ◽  
pp. 28
Author(s):  
Daniel Tabak
Keyword(s):  
Memory Hierarchy

scholarly journals Practical Wavelet Tree Construction

2021 ◽  
Vol 26 ◽  
pp. 1-67
Author(s):  
Patrick Dinklage ◽  
Jonas Ellert ◽  
Johannes Fischer ◽  
Florian Kurpicz ◽  
Marvin Löbel
Keyword(s):  
Parallel Algorithms ◽  
Shared Memory ◽  
Distributed Memory ◽  
Auxiliary Information ◽  
Parallel Computers ◽  
External Memory ◽  
Sequential Algorithms ◽  
Bottom Up ◽  
Memory Efficiency ◽  
Tree Construction

We present new sequential and parallel algorithms for wavelet tree construction based on a new bottom-up technique. This technique makes use of the structure of the wavelet trees—refining the characters represented in a node of the tree with increasing depth—in an opposite way, by first computing the leaves (most refined), and then propagating this information upwards to the root of the tree. We first describe new sequential algorithms, both in RAM and external memory. Based on these results, we adapt these algorithms to parallel computers, where we address both shared memory and distributed memory settings. In practice, all our algorithms outperform previous ones in both time and memory efficiency, because we can compute all auxiliary information solely based on the information we obtained from computing the leaves. Most of our algorithms are also adapted to the wavelet matrix , a variant that is particularly suited for large alphabets.

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