Graphics Processing Units (GPUs) have rapidly evolved to enable energy-efficient data-parallel computing for a broad range of scientific areas. While GPUs achieve exascale performance at a stringent power budget, they are also susceptible to soft errors, often caused by high-energy particle strikes, that can significantly affect the application output quality. Understanding the resilience of general purpose GPU applications is the purpose of this study. To this end, it is imperative to explore the range of application output by injecting faults at all the potential fault sites. This problem is especially challenging because unlike CPU applications, which are mostly single-threaded, GPGPU applications can contain hundreds to thousands of threads, resulting in a tremendously large fault site space - in the order of billions even for some simple applications. In this paper, we present a systematic way to progressively prune the fault site space aiming to dramatically reduce the number of fault injections such that assessment for GPGPU application error resilience can be practical. The key insight behind our proposed methodology stems from the fact that GPGPU applications spawn a lot of threads, however, many of them execute the same set of instructions. Therefore, several fault sites are redundant and can be pruned by a careful analysis of faults across threads and instructions. We identify important features across a set of 10 applications (16 kernels) from Rodinia and Polybench suites and conclude that threads can be first classified based on the number of the dynamic instructions they execute. We achieve significant fault site reduction by analyzing only a small subset of threads that are representative of the dynamic instruction behavior (and therefore error resilience behavior) of the GPGPU applications. Further pruning is achieved by identifying and analyzing: a) the dynamic instruction commonalities (and differences) across code blocks within this representative set of threads, b) a subset of loop iterations within the representative threads, and c) a subset of destination register bit positions. The above steps result in a tremendous reduction of fault sites by up to seven orders of magnitude. Yet, this reduced fault site space accurately captures the error resilience profile of GPGPU applications.
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A General Novel Parallel Framework for SPH-centric Algorithms
To date, large-scale fluid simulation with more details employing the Smooth Particle Hydrodynamics (SPH) method or its variants is ubiquitous in computer graphics and digital entertainment applications. Higher accuracy and faster speed are two key criteria evaluating possible improvement of the underlying algorithms within any available framework. Such requirements give rise to high-fidelity simulation with more particles and higher particle density that will unavoidably increase computational cost significantly. In this paper, we develop a new general GPGPU acceleration framework for SPH-centric simulations founded upon a novel neighbor traversal algorithm. Our novel parallel framework integrates several advanced characteristics of GPGPU architecture (e.g., shared memory and register memory). Additionally, we have designed a reasonable task assignment strategy, which makes sure that all the threads from the same CTA belong to the same cell of the grid. With this organization, big bunches of continuous neighboring data can be loaded to the shared memory of a CTA and used by all its threads. Our method has thus low global-memory bandwidth consumption. We have integrated our method into both WCSPH and PCISPH, that are two improved variants in recent years, and demonstrated its performance with several scenarios involving multiple-fluid interaction, dam break, and elastic solid. Through comprehensive tests validated in practice, our work can exhibit up to 2.18x speedup when compared with other state-of-the-art parallel frameworks.
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- Award ID(s):
- 1715985
- NSF-PAR ID:
- 10297833
- Date Published:
- Journal Name:
- Proceedings of the ACM on Computer Graphics and Interactive Techniques
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 2577-6193
- Page Range / eLocation ID:
- 1 to 16
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3321360
