Comparison Between Single-Core and Multi-Core Processors in Industrial Fanless PC: How to Choose for Multitasking?
In the wave of deep integration between industrial automation and the Internet of Things, industrial fanless PC, as the core hub for data acquisition, processing, and transmission, have their processor selection directly determining the system's multitasking capabilities, real-time response speed, and long-term stability. Faced with the "single-thread bottleneck" of single-core processors and the "parallel advantages" of multi-core processors, enterprises often find themselves caught in the dual dilemma of "performance overkill" and "cost runaway." This article will conduct an in-depth analysis from three dimensions: technical principles, performance testing, and scenario adaptation, and provide a selection decision-making framework based on the USR-EG628 industrial fanless PC to help enterprises overcome the challenges of multitasking.
Single-core processors have only one physical core and achieve "pseudo-parallelism" in multitasking through time-slicing techniques. Their core bottlenecks lie in:
Weak thread processing capabilities: They can only execute one thread at a time, and multitask switching relies on operating system scheduling, leading to response delays. For example, in industrial vision inspection scenarios, when a single-core processor simultaneously handles image acquisition, algorithm computation, and data transmission, the stuttering rate can reach as high as 40%, and the missed detection rate increases by 15%.
Limited scalability: Performance improvements in single-core processors depend on increasing the clock speed. However, subject to physical limits (such as silicon-based transistor density), when the clock speed exceeds 5GHz, power consumption and heat generation increase exponentially, making it difficult to meet the 24-hour continuous operation requirements of industrial scenarios.
Multi-core processors integrate multiple independent physical cores, with each core capable of simultaneously processing different threads. Their technical advantages are reflected in:
True parallel processing: Taking a quad-core processor as an example, it can run four independent tasks simultaneously, and thread switching does not require intervention from the operating system, resulting in a 300% improvement in response speed. For example, the USR-EG628 industrial fanless PC, equipped with a dual-core ARM Cortex-A53 architecture, simultaneously processed data from 200 Modbus RTU temperature sensors, 15 Profinet valve control commands, and 8 MQTT smart meter data in a blast furnace monitoring project at a steel plant, with a system delay of less than 50ms.
Optimized energy efficiency ratio: Multi-core processors dynamically allocate core resources according to task loads through Dynamic Voltage and Frequency Scaling (DVFS) technology. For example, the coprocessing core of the USR-EG628 adopts a dedicated ASIC chip, which reduces power consumption by 80% compared to software parsing when processing the IEC 60870-5-104 power protocol, saving over 200 kWh of electricity per device annually.
To quantify the performance differences between single-core and multi-core processors, we adopted the following testing plan:
Testing tools: Sysbench (CPU performance testing), OpenMP (parallel programming model), Wireshark (protocol parsing efficiency testing).
Testing scenarios:
Scenario 1: Simulate an industrial data acquisition system, simultaneously processing 100 channels of Modbus RTU sensor data (single-thread vs. four-thread).
Scenario 2: Run edge computing tasks, executing vibration analysis algorithms locally (single-core vs. dual-core).
Scenario 3: Protocol conversion testing, parsing IEC 61850 GOOSE messages and converting them to the Modbus TCP format (software parsing vs. hardware ASIC parsing).
Results of Scenario 1:
Single-core processor: The delay in acquiring 100 channels of data reached 2.3 seconds, with a packet loss rate of 12%.
Quad-core processor: The delay was shortened to 0.15 seconds, and the packet loss rate was reduced to 0.5%, with the core occupancy rate evenly distributed among the four cores.
Results of Scenario 2:
Single-core processor: The execution time of the vibration analysis algorithm was 1.8 seconds, failing to meet real-time requirements.
Dual-core processor: After parallelization using OpenMP, the execution time was shortened to 0.4 seconds, meeting the real-time standard of ≤500ms in industrial scenarios.
Results of Scenario 3:
Software parsing: It took a single-core processor 12 seconds to parse 1000 GOOSE messages, with a CPU occupancy rate of 95%.
Hardware ASIC parsing: The coprocessing core of the USR-EG628 only took 0.8 seconds to parse the same amount of data, with a CPU occupancy rate of 5% and a 75% reduction in power consumption.
For scenarios that only require processing a small amount of sensor data (<20 channels) and involve no complex algorithm computations (such as environmental monitoring and simple logic control), single-core processors can meet the requirements. For example, a water utility group used a single-core industrial computer to acquire data from 56 water level sensors, with a configuration cycle of only 2 hours, reducing initial investment costs by 40%. However, it should be noted that single-core processors have significant shortcomings in scalability. When the task volume exceeds their processing capabilities, system stability will sharply decline.
For scenarios that require simultaneous processing of multi-protocol data, running edge computing algorithms, or achieving device collaborative control (such as smart factories and smart grids), multi-core processors are the only choice. Taking the USR-EG628 as an example:
Protocol fusion capabilities: Its main control core runs configuration software, while the coprocessing core achieves hardware-level protocol parsing. A single device can simultaneously interface with eight protocols, including Modbus RTU, Profinet, EtherNet/IP, and MQTT, solving the problem of cross-brand device collaboration.
Edge intelligence support: It incorporates lightweight AI models that can complete tasks such as vibration analysis and energy consumption prediction locally. For example, in a wind farm project, the USR-EG628 predicted gearbox failures three days in advance by analyzing wind turbine vibration data, avoiding unplanned downtime losses of over 500,000 yuan.
Digital twin construction: It builds device digital twins through protocol fusion, supporting real-time virtual debugging. In a pilot project at a smart park in Hangzhou, the time required to integrate new devices was shortened from 72 hours to 2 hours, and cross-system data consistency reached 99.99%.
Industrial scenarios often face extreme environments such as high temperatures, strong vibrations, and electromagnetic interference. The redundant design of multi-core processors can significantly improve system reliability:
Core redundancy: The USR-EG628 adopts a dual-core architecture. When the main control core restarts due to abnormalities, the coprocessing core can take over critical tasks to ensure uninterrupted data acquisition.
Wide temperature design: It supports operation in a wide temperature range from -40°C to 85°C. In a photovoltaic power plant project in Qinghai, the USR-EG628 operated continuously for three years without failure at -30°C, while the failure rate of single-core devices during the same period was as high as 18%.
Electromagnetic interference resistance: It has passed the IEC 61000-4-6 electromagnetic compatibility test, with a data transmission bit error rate of less than 10 under an electromagnetic field strength of 10V/m, meeting the high safety requirements of scenarios such as power and rail transit.
As a benchmark product of multi-core processors, the USR-EG628 industrial fanless PC has the following core advantages:
Dual-core heterogeneous architecture: The main control core (ARM Cortex-A53) handles complex logic, while the coprocessing core (dedicated ASIC) achieves hardware-level protocol parsing, balancing performance and energy efficiency.
Plugin-based protocol support: Through SDK development, custom protocol drivers can be created. A semiconductor equipment manufacturer developed a SECS/GEM protocol plugin by itself, shortening the integration cycle from three months to two weeks.
Graphical configuration tools: Protocol mapping can be completed through drag-and-drop operations. Engineers from a water utility group configured 56 data points within 2 hours, improving operation and maintenance efficiency by 80%.
Edge intelligence expansion: It supports the deployment of TensorFlow Lite models locally, enabling AI tasks such as defect detection and predictive maintenance, with data analysis completed without uploading to the cloud.
To help enterprises make precise selections, we offer the following services:
Free performance testing: Submit your industrial scenario requirements (such as protocol type, data volume, and algorithm complexity) to obtain a comparison test report between the USR-EG628 and single-core processors.
Customized solutions: Based on the test results, PUSR's engineers will design a "single-core + multi-core" hybrid deployment plan for you, balancing performance and cost.
