Toward Real-Time Ray Tracing

Deng, Yangdong; Ni, Yufei; Li, Zonghui; Mu, Shuai; Zhang, Wenjun

doi:10.1145/3104067

Cited by 37 publications

(4 citation statements)

References 73 publications

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“…RobotPerf allows for the evaluation of benchmarks on a variety of hardware platforms, including general-purpose CPUs and GPUs, reconfigurable FPGAs, and specialized accelerators (e.g., ray tracing accelerators [81]). Benchmarking robotic workloads on heterogeneous platforms is vital to evaluate their respective capabilities and limitations.…”

Section: Platform Independence and Portabilitymentioning

confidence: 99%

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

Mayoral-Vilches¹,

Neuman

Plancher

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We introduce RobotPerf, a vendor-agnostic benchmarking suite designed to evaluate robotics computing performance across a diverse range of hardware platforms using ROS 2 as its common baseline. The suite encompasses ROS 2 packages covering the full robotics pipeline and integrates two distinct benchmarking approaches: black-box testing, which measures performance by eliminating upper layers and replacing them with a test application, and grey-box testing, an application-specific measure that observes internal system states with minimal interference. Our benchmarking framework provides ready-to-use tools and is easily adaptable for the assessment of custom ROS 2 computational graphs. Drawing from the knowledge of leading robot architects and system architecture experts, RobotPerf establishes a standardized approach to robotics benchmarking. As an open-source initiative, RobotPerf remains committed to evolving with community input to advance the future of hardware-accelerated robotics.

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Section: Platform Independence and Portabilitymentioning

confidence: 99%

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

Mayoral-Vilches¹,

Neuman

Plancher

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…The finer the modeled environment, the higher is the corresponding prediction accuracy and the longer is the calculation time. The binary space partitioning (BSP) algorithm [22] was applied to accelerate the "image method" based RT simulation. Thus, owing to the parallel processing and the acceleration technique, the running time of the RT simulation tasks were greatly shortened.…”

Section: Rt Simulation In An Urban Scenario At 300 Ghzmentioning

confidence: 99%

Impact of Meteorological Attenuation on Channel Characterization at 300 GHz

Lai

Guan

et al. 2020

Electronics

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Terahertz (THz) communication is a key candidate for the upcoming age of beyond-fifth-generation mobile networks (B5G) or sixth-generation mobile networks (6G) in the next decade and can achieve ultra-high data rates of dozens of gigabits or even terabits per second. As the carrier frequency increases from radio frequency (RF) to the THz band, the impact of meteorological factors on the wireless link is expected to become more pronounced. In this work, we first provide an overview of the attenuation caused by atmospheric gases, fog, and rain on terrestrial THz wireless communications using the recommendations of the International Telecommunication Union-Radiocommunication (ITU-R). Measured data from the literature are used to predict the attenuation caused by snow. Because unfavorable weather conditions may harm sensitive measurement equipment, ray-tracing (RT) simulations are sometimes used as an alternative to extend sparse empirical data. In this study, the terrestrial channel in an urban scenario at 300 GHz, with a bandwidth of 8 GHz, is characterized using RT simulations under different meteorological factors. The key performance parameters are explored, including path loss (PL), Rician K-factor (KF), root-mean-square (RMS) delay spread (DS), and four angular spreads. The channel characteristics under different meteorological conditions studied in this work are expected to aid the design of future outdoor terrestrial THz communications.

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“…The optimization direction of the software layer includes data structure, optics, computational geometry and statistics [5]. Compared with optimization on the software layer, optimization on the hardware layer improves the rendering efficiency more significantly [6].…”

Section: Introductionmentioning

confidence: 99%

Concurrent pipeline rendering scheme based on GPU multi-queue and partitioning images

Qiu

Zhang

et al. 2023

International Conference on Optics and Machine Vision (ICOMV 2023)

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Compared with rasterization rendering, ray tracing rendering can improve the image's visual effect and make the image look more realistic. Real-time ray tracing requires very high computing power of graphics processing unit (GPU). When the number of GPUs is limited and the performance of a single GPU cannot be fully utilized, high rendering latency will occur. In this paper, we propose Multi-Queue Concurrent Pipeline Rendering (MQCPR), a novel ray-tracing parallel rendering scheme based on GPU multi-queue. This scheme divides the image area into multiple parts and uses multi-queue of GPU to enable the computation and transmission tasks in the rendering process to be executed simultaneously, which can maximize the performance of a single GPU and improve the graphics rendering speed. MQCPR may keep the GPU busy to make full use of the GPU resources. Experiments illustrate that in the case of a single GPU, compared with the single queue serial rendering scheme, the number of frames per second (FPS) is increased by 1.5 times after using MQCPR.

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Toward Real-Time Ray Tracing

Cited by 37 publications

References 73 publications

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

Impact of Meteorological Attenuation on Channel Characterization at 300 GHz

Concurrent pipeline rendering scheme based on GPU multi-queue and partitioning images

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