Quality-Driven Dynamic VVC Frame Partitioning for Efficient Parallel Processing

KaHIP v3.00 -- Karlsruhe High Quality Partitioning -- User Guide

This paper severs as a user guide to the graph partitioning framework KaHIP (Karlsruhe High Quality Partitioning). We give a rough overview of the techniques used within the framework and describe the user interface as well as the file formats used. Moreover, we provide a short description of the current library functions provided within the framework. Since version 3.00 we support multilevel partitioning, memetic algorithms, distributed and shared-memory parallel algorithms, node separator and ordering algorithms, edge partitioning algorithms as well as ILP solvers.

Hardware locality-aware partitioning and dynamic load-balancing of unstructured meshes for large-scale scientific applications

We present an open-source topology-aware hierarchical unstructured mesh partitioning and load-balancing tool TreePart. The framework provides powerful abstractions to automatically detect and build hierarchical MPI topology resembling the hardware at runtime. Using this information it intelligently chooses between shared and distributed parallel algorithms for partitioning and load-balancing. It provides a range of partitioning methods by interfacing with existing shared and distributed memory parallel partitioning libraries. It provides powerful and scalable abstractions like one-sided distri…

AMP4EC：面向边缘计算环境中高效深度学习推理的自适应模型划分框架

AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments

本文提出自适应模型划分框架 AMP4EC，通过实时资源监控、动态划分和加权任务调度优化边缘深度学习推理，实现延迟降78%、吞吐量升414%，适应不同资源场景。

Distributed Edge Partitioning for Graph Processing

The availability of larger and larger graph datasets, growing exponentially over the years, has created several new algorithmic challenges to be addressed. Sequential approaches have become unfeasible, while interest on parallel and distributed algorithms has greatly increased. Appropriately partitioning the graph as a preprocessing step can improve the degree of parallelism of its analysis. A number of heuristic algorithms have been developed to solve this problem, but many of them subdivide the graph on its vertex set, thus obtaining a vertex-partitioned graph. Aim of this paper is to explor…

Recursive Multi-Section on the Fly: Shared-Memory Streaming Algorithms for Hierarchical Graph Partitioning and Process Mapping

Partitioning a graph into balanced blocks such that few edges run between blocks is a key problem for large-scale distributed processing. A current trend for partitioning huge graphs are streaming algorithms, which use low computational resources. In this work, we present a shared-memory streaming multi-recursive partitioning scheme that performs recursive multi-sections on the fly without knowing the overall input graph. Our approach has a considerably lower running time complexity in comparison with state-of-the-art non-buffered one-pass partitioning algorithms for the standard graph partiti…

Game Theoretic Iterative Partitioning for Dynamic Load Balancing in Distributed Network Simulation

High fidelity simulation of large-sized complex networks can be realized on a distributed computing platform that leverages the combined resources of multiple processors or machines. In a discrete event driven simulation, the assignment of logical processes (LPs) to machines is a critical step that affects the computational and communication burden on the machines, which in turn affects the simulation execution time of the experiment. We study a network partitioning game wherein each node (LP) acts as a selfish player. We derive two local node-level cost frameworks which are feasible in the se…