TOD-Tree: Task-Overlapped Direct Send Tree Image Compositing for Hybrid MPI Parallelism and GPUs

Modern supercomputers have thousands of nodes, each with CPUs and/or GPUs capable of several teraflops. However, the network connecting these nodes is relatively slow, on the order of gigabits per second. For time-critical workloads such as interactive visualization, the bottleneck is no longer comp...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics Vol. 23; no. 6; pp. 1677 - 1690
Main Authors: Pascal Grosset, A. V., Prasad, Manasa, Christensen, Cameron, Knoll, Aaron, Hansen, Charles
Format: Journal Article
Language:English
Published: United States IEEE 01.06.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Communication
Computation
Computer Science
Data visualization
Distributed volume rendering
Graphics processing units
image compositing
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Message systems
Nodes
Parallel processing
Rendering (computer graphics)
Supercomputers
Workflow
ISSN:1077-2626, 1941-0506
Online Access:Get full text
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Summary:Modern supercomputers have thousands of nodes, each with CPUs and/or GPUs capable of several teraflops. However, the network connecting these nodes is relatively slow, on the order of gigabits per second. For time-critical workloads such as interactive visualization, the bottleneck is no longer computation but communication. In this paper, we present an image compositing algorithm that works on both CPU-only and GPU-accelerated supercomputers and focuses on communication avoidance and overlapping communication with computation at the expense of evenly balancing the workload. The algorithm has three stages: a parallel direct send stage, followed by a tree compositing stage and a gather stage. We compare our algorithm with radix-k and binary-swap from the IceT library in a hybrid OpenMP/MPI setting on the Stampede and Edison supercomputers, show strong scaling results and explain how we generally achieve better performance than these two algorithms. We developed a GPU-based image compositing algorithm where we use CUDA kernels for computation and GPU Direct RDMA for inter-node GPU communication. We tested the algorithm on the Piz Daint GPU-accelerated supercomputer and show that we achieve performance on par with CPUs. Last, we introduce a workflow in which both rendering and compositing are done on the GPU.
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NA0002375; SC0007446
USDOE National Nuclear Security Administration (NNSA)
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2016.2542069