-
A Fine-Grained Hybrid CPU-GPU Algorithm for Betweenness Centrality Computations
Betweenness centrality (BC) is an important graph analytical application...
read it
-
GPGPU Acceleration of the KAZE Image Feature Extraction Algorithm
The recently proposed open-source KAZE image feature detection and descr...
read it
-
GPU Parallelization of Policy Iteration RRT#
Sampling-based planning has become a de facto standard for complex robot...
read it
-
Towards Fine-Grained Billing For Cloud Networking
We revisit multi-tenant network virtualization in data centers, and make...
read it
-
GPU Parallel Computation of Morse-Smale Complexes
The Morse-Smale complex is a well studied topological structure that rep...
read it
-
A Multi-signal Variant for the GPU-based Parallelization of Growing Self-Organizing Networks
Among the many possible approaches for the parallelization of self-organ...
read it
-
Performance optimization and modeling of fine-grained irregular communication in UPC
The UPC programming language offers parallelism via logically partitione...
read it
Exploration of Fine-Grained Parallelism for Load Balancing Eager K-truss on GPU and CPU
In this work we present a performance exploration on Eager K-truss, a linear-algebraic formulation of the K-truss graph algorithm. We address performance issues related to load imbalance of parallel tasks in symmetric, triangular graphs by presenting a fine-grained parallel approach to executing the support computation. This approach also increases available parallelism, making it amenable to GPU execution. We demonstrate our fine-grained parallel approach using implementations in Kokkos and evaluate them on an Intel Skylake CPU and an Nvidia Tesla V100 GPU. Overall, we observe between a 1.261. 48x improvement on the CPU and a 9.97-16.92x improvement on the GPU due to our fine-grained parallel formulation.
READ FULL TEXT
Comments
There are no comments yet.