arXiv subjects

Mingyu Yan

Publications and source records attributed to Mingyu Yan.

44 records · Page 3Linked to original sources

arXiv2022

Alleviating Datapath Conflicts and Design Centralization in Graph Analytics Acceleration

Previous graph analytics accelerators have achieved great improvement on throughput by alleviating irregular off-chip memory accesses. However, on-chip side datapath conflicts and design centralization have become the critical issues hindering further throughput improvement. In this paper, a general solution, Multiple-stage Decentralized Propagation network (MDP-network), is proposed to address these issues, inspired by the key idea of trading latency for throughput. Besides, a novel High throughput Graph analytics accelerator, HiGraph, is proposed by deploying MDP-network to address each issue in practice. The experiment shows that compared with state-of-the-art accelerator, HiGraph achieves up to 2.2x speedup (1.5x on average) as well as better scalability.

cs.AR↗

arXiv2021

Sampling methods for efficient training of graph convolutional networks: A survey

Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GCN performs well compared with other methods, it still faces challenges. Training a GCN model for large-scale graphs in a conventional way requires high computation and storage costs. Therefore, motivated by an urgent need in terms of efficiency and scalability in training GCN, sampling methods have been proposed and achieved a significant effect. In this paper, we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN. To highlight the characteristics and differences of sampling methods, we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories. Finally, we discuss some challenges and future research directions of the sampling methods.

cs.LG↗

arXiv2021

RISC-NN: Use RISC, NOT CISC as Neural Network Hardware Infrastructure

Neural Networks (NN) have been proven to be powerful tools to analyze Big Data. However, traditional CPUs cannot achieve the desired performance and/or energy efficiency for NN applications. Therefore, numerous NN accelerators have been used or designed to meet these goals. These accelerators all fall into three categories: GPGPUs, ASIC NN Accelerators and CISC NN Accelerators. Though CISC NN Accelerators can achieve considerable smaller memory footprint than GPGPU thus improve energy efficiency; they still fail to provide same level of data reuse optimization achieved by ASIC NN Accelerators because of the inherited poor pragrammability of their CISC architecture. We argue that, for NN Accelerators, RISC is a better design choice than CISC, as is the case with general purpose processors. We propose RISC-NN, a novel many-core RISC-based NN accelerator that achieves high expressiveness and high parallelism and features strong programmability and low control-hardware costs. We show that, RISC-NN can implement all the necessary instructions of state-of-the-art CISC NN Accelerators; in the meantime, RISC-NN manages to achieve advanced optimization such as multiple-level data reuse and support for Sparse NN applications which previously only existed in ASIC NN Accelerators. Experiment results show that, RISC-NN achieves on average 11.88X performance efficiency compared with state-of-the-art Nvidia TITAN Xp GPGPU for various NN applications. RISC-NN also achieves on average 1.29X, 8.37X and 21.71X performance efficiency over CISC-based TPU in CNN, MLP and LSTM applications, respectively. Finally, RISC-NN can achieve additional 26.05% performance improvement and 33.13% energy reduction after applying pruning for Sparse NN applications.

cs.AR↗

arXiv2020

Hurricanes on tidally locked terrestrial planets: Fixed surface temperature experiments

In this work, we study the presence of hurricanes on exoplanets. Tidally locked terrestrial planets around M dwarfs are the main targets of space missions looking to discover habitable exoplanets. The question of whether hurricanes can form on this kind of planet is important for determining their climate and habitability. Using a high-resolution global atmospheric circulation model, we investigated whether there are hurricanes on tidally locked terrestrial planets under fixed surface temperatures. The relevant effects of the planetary rotation rate, surface temperature, and bulk atmospheric compositions were examined. We find that hurricanes can form on the planets but not on all of them. For planets near the inner edge of the habitable zone of late M dwarfs, there are more numerous and stronger hurricanes on both day and night sides. For planets in the middle and outer ranges of the habitable zone, the possibility of hurricane formation is low or even close to zero, as has been suggested in recent studies. Earth-based hurricane theories are applicable to tidally locked planets only when the atmospheric compositions are similar to that of Earth. However, if the background atmosphere is lighter than H2O, hurricanes can hardly be produced because convection is always inhibited due to the effect of the mean molecular weight, similarly to the case of Saturn. These results have broad implications on the precipitation, ocean mixing, climate, and atmospheric characterization of tidally locked planets. Finally, A test with a coupled slab ocean and an Earth-like atmosphere in a tide-locked orbit of ten Earth days demonstrates that there are also hurricanes present in the experiment.

astro-ph.EP↗

arXiv2020

Rubik: A Hierarchical Architecture for Efficient Graph Learning

Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs. However, learning from graphs is non-trivial because of its mixed computation model involving both graph analytics and neural network computing. To this end, we decompose the GCN learning into two hierarchical paradigms: graph-level and node-level computing. Such a hierarchical paradigm facilitates the software and hardware accelerations for GCN learning. We propose a lightweight graph reordering methodology, incorporated with a GCN accelerator architecture that equips a customized cache design to fully utilize the graph-level data reuse. We also propose a mapping methodology aware of data reuse and task-level parallelism to handle various graphs inputs effectively. Results show that Rubik accelerator design improves energy efficiency by 26.3x to 1375.2x than GPU platforms across different datasets and GCN models.

cs.AR↗

arXiv2020

Characterizing and Understanding GCNs on GPU

Graph convolutional neural networks (GCNs) have achieved state-of-the-art performance on graph-structured data analysis. Like traditional neural networks, training and inference of GCNs are accelerated with GPUs. Therefore, characterizing and understanding the execution pattern of GCNs on GPU is important for both software and hardware optimization. Unfortunately, to the best of our knowledge, there is no detailed characterization effort of GCN workloads on GPU. In this paper, we characterize GCN workloads at inference stage and explore GCN models on NVIDIA V100 GPU. Given the characterization and exploration, we propose several useful guidelines for both software optimization and hardware optimization for the efficient execution of GCNs on GPU.

cs.DC↗

arXiv2020

HyGCN: A GCN Accelerator with Hybrid Architecture

In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU. Guided by the characterization, we design a GCN accelerator, HyGCN, using a hybrid architecture to efficiently perform GCNs. Specifically, first, we build a new programming model to exploit the fine-grained parallelism for our hardware design. Second, we propose a hardware design with two efficient processing engines to alleviate the irregularity of Aggregation phase and leverage the regularity of Combination phase. Besides, these engines can exploit various parallelism and reuse highly reusable data efficiently. Third, we optimize the overall system via inter-engine pipeline for inter-phase fusion and priority-based off-chip memory access coordination to improve off-chip bandwidth utilization. Compared to the state-of-the-art software framework running on Intel Xeon CPU and NVIDIA V100 GPU, our work achieves on average 1509$\times$ speedup with 2500$\times$ energy reduction and average 6.5$\times$ speedup with 10$\times$ energy reduction, respectively.

cs.DC↗

arXiv2015

Fair Packet Scheduling in Network on Chip

Interconnection networks of parallel systems are used for servicing traf- fic generated by different applications, often belonging to different users. When multiple traffic flows contend for channel bandwidth, the scheduling algorithm regulating the access to that channel plays a key role in ensur- ing that each flow obtains the required quality of service. Fairness is a highly desirable property for a scheduling algorithm. We show that using the Relative Fairness Bound as a fairness measure may lead to decrease in throughput and increase in latency. We propose an alternative metric to evaluate the fairness and avoid the drawback of Relative Fairness Bound.

cs.DC↗