RT info:eu-repo/semantics/conferenceObject
T1 Programming and Mapping for Mixed Heterogeneous Devices: The Case of Optical Flow
A1 Alonso Pascual, Sergio
A1 González Escribano, Arturo
K1 Informática
K1 Heterogeneous programming, Streaming, Parallel pipeline, Optical Flow
K1 1203.17 Informática
K1 3304 Tecnología de Los Ordenadores
AB Current parallel systems are increasingly heterogeneous, mixing devices of different types and computing capabilities. Exploiting multiple different devices for the same application continues to be a challenge that ranges from technical problems related to synchronizing and communicating diverse devices to problems of load distribution and flexibility to adjust the computation to the platform resources. In this work, we study the problem of using and extending a heterogeneous portability layer to program and adapt HSOpticalFlow to heterogeneous platforms. HSOpticalFlow is a streaming application to estimate the apparent movement of objects in a sequence of images. It is a simple but characteristic example of the structure of applications based on multilevel ILS (Iterative Loop Stencil), also known as multi-grid methods, applied to a sequence of inputs. Starting from the original CUDA reference code, we present a methodology and programming techniques based on the Controller programming model to implement it as a pipeline among multiple devices. We discuss a technique to determine a proper work partition and mapping for a set of devices. This allows for building very efficient parallel solutions, using similar devices or taking advantage of devices with lower computing power, to reduce the load and increase the productivity of more powerful ones. We present the results of an experimental study using several GPUs of different vendors, architectures, and generations, showing that this solution allows combinations of devices to be efficiently exploited to improve performance. Specifically, the results include speedups of 1.91x using two NVIDIA A100 GPUs and 1.21x using one NVIDIA V100 GPU and one AMD WX9100 GPU, which is about 3x slower than the NVIDIA GPU for this application.
PB IEEE
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/75764
UL https://uvadoc.uva.es/handle/10324/75764
LA eng
NO 2025 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), Turin, Italy, 2025, pp. 187-195, doi: 10.1109/PDP66500.2025.00033
NO Producción Científica
DS UVaDOC
RD 02-jun-2025