GraphCL: A Framework for Execution of Data-Flow Graphs on Multi-Device Platforms

Moreń, Konrad; Göhringer, Diana

doi:10.1109/pdp55904.2022.00026

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Then, the last step of the profiling phase, Step C, is performed. At this point, the acceleration between both offloading modes is computed, determining the best strategy to be used with the device being profiled: Thus, values higher than 1.0 indicate that the device has a beneficial behavior when facing workload splitting strategies, allowing an increase in throughputs by taking advantage of multiple command queues, overlap between computation and communication as well as appropriate interleaving between management and computation, as demonstrated in previous studies [17,19,[21][22][23][24]. And therefore, values lower than 1.0 indicate that it suffers penalization for device management and chunk synchronization, sharing of CPU usage with the simulator itself or other tasks and even an indication of very short execution times, where the generation of multiple chunks is usually counterproductive.…”

Section: Mash Algorithmmentioning

confidence: 98%

Mashing load balancing algorithm to boost hybrid kernels in molecular dynamics simulations

Nozal

Bosque

2022

J Supercomput

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The path to the efficient exploitation of molecular dynamics simulators is strongly driven by the increasingly intensive use of accelerators. However, they suffer performance portability issues, making it necessary both to achieve technological combinations that allow taking advantage of each programming model and device, and to define more effective load distribution strategies that consider the simulation conditions. In this work, a new load balancing algorithm is presented, together with a set of optimizations to support hybrid co-execution in a runtime system for heterogeneous computing. The new extended design enables the exploitation of custom kernels and acceleration technologies altogether, being encapsulated for the rest of the runtime and its scheduling system. With this support, Mash algorithm allows to simultaneously leverage different workload distribution strategies, benefiting from the most advantageous one per device and technology. Experiments show that these proposals achieve an efficiency close to 0.90 and an energy efficiency improvement around 1.80 over the original optimized version.

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Section: Mash Algorithmmentioning

confidence: 98%

Mashing load balancing algorithm to boost hybrid kernels in molecular dynamics simulations

Nozal

Bosque

2022

J Supercomput

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“…Due to the complexity of graph structure, traditional contrastive learning methods cannot be easily applied directly to graph data. Graph contrastive learning [54][55][56] methods make similar graphs closer by designing effective graph matching or clustering loss.…”

Section: Contrastive Learningmentioning

confidence: 99%

Enhancing Grid Tagging Schema with Hybrid Contrastive Learning for Aspect-level Sentiment Triplet Extraction

Zheng,

Wang,

et al. 2023

Preprint

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Aspect-based sentiment triplet extraction (ASTE) is a fine-grained opinion mining to jointly extract a triplet consisting of aspect terms, opinion terms, and sentiment polarity. Previous works tried incorporating extra dependency information by stacking complex graph convolution networks (GCN) on transformers to learn the relationship between the aspects and their associated opinion. While the number of existing datasets is still insufficient, the GCN-accompanied redundant parameters in each method require more samples to provide supervised information, leading to the model being trapped by overfitting. Instead of stacking GCNs, this study proposes an enhanced grid tagging scheme with hybrid contrastive learning, exploiting a self-supervised training approach from the negative samples. Specifically, sentiment-aware contrastive learning (SCL) is applied by introducing word-level continuous sentiment information in the valence-arousal space. Syntactic-aware graph contrastive learning (SGCL) is used to guide the language model to gain the ability to capture dependencies. Supervised tagging contrastive learning (STCL) is further adapted to guide the language model to distinguish both positive and negative samples. Hybrid contrastive learning can provide extra supervision information but only introduce tolerable parameters. Extensive experimental results on the ASTE-Data-V2 datasets show that the proposed model significantly outperforms previous baselines.

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GraphCL: A Framework for Execution of Data-Flow Graphs on Multi-Device Platforms

Cited by 2 publications

References 17 publications

Mashing load balancing algorithm to boost hybrid kernels in molecular dynamics simulations

Mashing load balancing algorithm to boost hybrid kernels in molecular dynamics simulations

Enhancing Grid Tagging Schema with Hybrid Contrastive Learning for Aspect-level Sentiment Triplet Extraction

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