SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC On-Demand

Combined Simulation & Real-Time Visualization
Presentation
Media
Proximity Computation on Heterogeneous Computing Systems
Duksu Kim (Korea Advanced Institute of Science and Technology)
This session will introduce a novel, optimization-based workload distribution algorithm that exploits heterogeneous systems to accelerate various proximity queries. To represent complicated performance relationships between computing resources a ...Read More

This session will introduce a novel, optimization-based workload distribution algorithm that exploits heterogeneous systems to accelerate various proximity queries. To represent complicated performance relationships between computing resources and different computations of proximity queries, we propose a simple model that measures the expected running time of these computations. Based on this model, we formulate an optimization problem that minimizes the largest time spent on computing resources, and propose a novel, iterative LP-based scheduling algorithm. We apply our method into various proximity queries used in five different applications that have different characteristics. Our method achieves an order of magnitude performance improvement by using four different GPUs and two hexa-core CPUs over using a hexa-core CPU only. In addition, we integrate our expected running time model with a work stealing method and achieve 16% performance improvement on average over the basicl work stealing method.

  Back
 
Keywords:
Combined Simulation & Real-Time Visualization, GTC 2013 - ID S3166
Streaming:
Download:
Computational Fluid Dynamics
Presentation
Media
Out-of-Core Proximity Computation on GPU for Particle-Based Fluid Simulations
Duksu Kim ((KISTI) Korea Institute of Science and Technology Information)
Lean how to use your GPU for massive-scale particle-based fluid simulations that require a larger amount of memory space than the video memory. We introduce a novel GPU-based neighbor search algorithm used in particle-based fluid simulations such as ...Read More
Lean how to use your GPU for massive-scale particle-based fluid simulations that require a larger amount of memory space than the video memory. We introduce a novel GPU-based neighbor search algorithm used in particle-based fluid simulations such as SPH. With the proposed method, we can efficiently handle a massive-scale particle-based fluid simulation with a limited GPU video memory in out-of-core manner. We have demonstrated that our method robustly handles massive-scale benchmark scenes consisting of up to 65 million particles and requires up to 16 GB memory by using a GPU having only 3 GB memory. It shows up to 26 times higher performance compared to using NVIDIA's mapped memory technique and 51 times higher performance compared to using a CPU core.  Back
 
Keywords:
Computational Fluid Dynamics, Developer - Algorithms, Computational Physics, Real-Time Graphics, GTC 2015 - ID S5116
Streaming:
Download:
Computational Physics
Presentation
Media
Out-of-Core Proximity Computation on GPU for Particle-based Fluid Simulations
Duksu Kim ((KISTI) Korea Institute of Science and Technology and Information)
We present an out-of-core proximity computation method, commonly used for particle-based fluid simulations, to handle a massive-scale simulation requiring a larger memory space than a GPU has. We have demonstrated that our method robustly handles up ...Read More
We present an out-of-core proximity computation method, commonly used for particle-based fluid simulations, to handle a massive-scale simulation requiring a larger memory space than a GPU has. We have demonstrated that our method robustly handles up to 65 M particles and requires up to 16 GB memory by using a GPU having only 3 GB memory. It's up to 51 X higher performance than using a CPU core. This high performance with a limited video memory space is achieved mainly thanks to the high accuracy of our memory estimation method.  Back
 
Keywords:
Computational Physics, Developer - Algorithms, GTC 2015 - ID P5113
Download:
 
 
NVIDIA - World Leader in Visual Computing Technologies
Copyright © 2017 NVIDIA Corporation Legal Info | Privacy Policy