FPGAs: The Secret Weapon Powering Next-Generation Cars

In the automotive industry, the pursuit of innovation is perpetual, underscored by the relentless evolution of technology, shifting consumer demands, and stringent regulatory standards. Amidst this dynamic landscape, the challenges confronting automakers are manifold, necessitating sophisticated solutions to navigate the complexities of modern vehicle design and functionality. At the forefront of this endeavor lies the indispensable role of Field-Programmable Gate Arrays (FPGAs), heralded as pivotal enablers in addressing some of the most pressing challenges faced by the automotive sector.

Adapting to Change with Agility and Efficiency

At the core of today’s automotive landscape lies the essential need for connectivity. Modern vehicles have transcended their traditional roles, evolving into interconnected ecosystems with digital systems and functionalities. However, orchestrating seamless communication among these varying components presents a formidable challenge. FPGAs, renowned for their unparalleled flexibility and adaptability, emerge as instrumental facilitators in ensuring cohesive integration and interoperability across diverse automotive systems, from infotainment and navigation to telematics and vehicle-to-everything (V2X) communication.

Alongside connectivity, automotive systems must exhibit robustness and resilience to withstand the rigors of real-world driving conditions. The demands placed on vehicular systems are demanding, encompassing extreme temperatures, mechanical vibrations, and electromagnetic interference. FPGAs are revered for their inherent robustness and reliability, capable of enduring the harshest environmental conditions while maintaining optimal performance. In safety-critical applications such as autonomous driving and advanced driver assistance systems (ADAS), the dependability of FPGAs is indispensable, safeguarding vehicle occupants and ensuring operational integrity.

Furthermore, the automotive industry experiences constant change, marked by evolving consumer preferences, emerging technologies, and regulatory requirements. To navigate this dynamic terrain, automakers must exhibit agility and adaptability, swiftly responding to market dynamics and technological advancements. FPGAs, characterized by their rapid reconfigurability and scalability, empower automakers to iterate and innovate expeditiously, ensuring alignment with evolving market demands and regulatory mandates.

Moreover, the imperative of energy efficiency and sustainability looms large in the automotive sector, necessitating judicious management of power consumption and data processing. FPGAs emerge as exemplars of efficiency, combining high-performance computing capabilities with minimal power consumption. Whether optimizing power management systems or processing sensor data in real-time, FPGAs epitomize the convergence of performance and efficiency, supporting the industry’s transition towards sustainable mobility solutions.

FPGAs: Shaping the Future of Mobility

FPGAs epitomize a paradigm of innovation and resilience within the automotive sector, underpinning the development of next-generation vehicles equipped to meet the demands of an increasingly interconnected and dynamic world. As the automotive industry continues to evolve, FPGAs will remain indispensable allies, driving advancements in connectivity, robustness, agility, and efficiency, and shaping the future of mobility for generations to come.

Effective driver development is crucial in the automotive industry as it directly impacts vehicle systems’ performance, reliability, and functionality. Drivers interface hardware devices, such as FPGAs, and the operating system, enabling seamless communication and control. A well-designed driver ensures optimal system operation, enhances safety, and facilitates the integration of new features and technologies. Additionally, efficient driver development streamlines the overall development process, reducing time-to-market and costs while enabling automakers to stay competitive in a rapidly evolving market.

Optimizing FPGAs: Why Driver Development Matters

Jungo’s WinDriver toolkit offers a comprehensive solution for driver development, significantly improving the efficiency, effectiveness, and cost-effectiveness of FPGA driver development. By providing a robust set of tools and resources, WinDriver simplifies the complexities of driver development, empowering developers to focus on innovation rather than mundane implementation details. WinDriver accelerates the development cycle, including automatic code generation, debugging tools, and built-in support for industry-standard protocols, ensuring compatibility and reliability across diverse automotive applications. 

By leveraging Jungo’s WinDriver toolkit, automakers can streamline driver development processes, reduce development costs, and accelerate time-to-market for innovative automotive solutions, thereby gaining a competitive edge in the fast-paced automotive industry landscape.

Ready to learn more about how FPGAs can revolutionize your automotive designs? Contact us today!

IOMMU vs. DMA

Understanding the Data Transfer Powerhouse Duo

In modern computing environments, efficient data transfer between hardware devices and system memory is crucial for optimal performance. Two key technologies utilized to facilitate this data movement are the Input-Output Memory Management Unit (IOMMU) and Direct Memory Access (DMA). While both mechanisms serve the same fundamental purpose of enabling efficient data transfers, they operate differently and cater to distinct use cases. In this article, we delve into the technical nuances that differentiate IOMMU and direct DMA.

Understanding Direct Memory Access (DMA)

Direct Memory Access (DMA) is a mechanism that allows hardware peripherals to transfer data directly to and from the system’s memory without involving the CPU. DMA is commonly employed in scenarios where frequent data transfers between devices and memory are required, such as disk I/O operations or network communication.

In a typical DMA operation, the peripheral device initiates the data transfer by sending a DMA request to the DMA controller. The DMA controller, in turn, coordinates the transfer by temporarily taking control of the system bus and accessing the memory directly. This bypasses the CPU, thereby improving overall system performance by reducing CPU overhead.

The Role of IOMMU

On the other hand, the Input-Output Memory Management Unit (IOMMU) is a hardware component responsible for managing memory access for I/O devices. It acts as a bridge between the physical addresses used by hardware devices and the virtual addresses used by the CPU.

One of the primary functions of the IOMMU is to provide memory protection and isolation for I/O devices. By mapping device-visible physical addresses to system memory, the IOMMU ensures that each device can only access its allocated memory regions, preventing unauthorized access and enhancing system security.

Additionally, the IOMMU enables address translation and remapping, allowing devices to access memory beyond the system’s physical address space. This feature is particularly useful in systems with large amounts of memory or when utilizing virtualization technologies, as it allows for efficient memory management and resource allocation.

Key Differences and Use Cases

The main difference between IOMMU and direct DMA lies in their approach to memory access and management:

Address Translation: In direct DMA, the device accesses memory using physical addresses directly, without translation. In contrast, the IOMMU performs address translation, mapping device-visible physical addresses to system memory addresses, thereby providing memory protection and isolation.

Memory Management: Direct DMA transfers data between devices and memory without CPU intervention, optimizing performance. Conversely, the IOMMU adds an additional layer of complexity by managing memory mappings and access permissions, which may introduce some overhead.

Use Cases: Direct DMA is well-suited for scenarios requiring high-performance data transfers, such as disk I/O or network communication, where minimizing CPU overhead is critical. On the other hand, the IOMMU is essential for ensuring memory protection, isolation, and address translation in systems with multiple I/O devices or in virtualized environments.

While both IOMMU and direct DMA serve the common goal of facilitating efficient data transfers between hardware devices and system memory, they operate using different mechanisms and cater to distinct use cases. Direct DMA prioritizes performance by allowing devices to access memory directly without CPU intervention, whereas the IOMMU adds a layer of memory management and protection, essential for ensuring system security and resource isolation. 

In our research, we found that performing DMA utilizing IOMMU components dramatically enhances the transfer rate. To utilize IOMMU components for DMA, enable the IOMMU on your system’s bios. The operating system will manage the DMA with the selected hardware. 

Optimizing Data Transfer

Exploring Interrupt and Polling Mechanisms in DMA

In computer architecture, optimizing data transfer is essential for system performance. Direct Memory Access (DMA) controllers enable high-speed data movement between peripherals and memory without constant CPU involvement. A key decision in configuring DMA transfers is selecting the transfer flow sequence.

Polling Method DMA

Polling-based DMA requires the CPU to repeatedly check the DMA controller’s status to determine when a transfer is complete. The CPU polls the DMA controller at set intervals, usually via a status register or memory location. Once the transfer finishes, the CPU resumes its tasks.

Polling-based DMA is simple to implement since it does not require complex interrupt handling. It also offers determinism, as the CPU controls when polling occurs, which benefits real-time systems. Additionally, it incurs lower overhead than interrupts because it avoids context switching.

However, polling keeps the CPU occupied, reducing availability for other tasks. In systems with frequent DMA operations, this can hurt performance. Polling also introduces latency, as the CPU may not immediately detect completed transfers, delaying subsequent tasks. Infrequent or unpredictable data transfers further waste CPU cycles, leading to inefficiency.

Interrupt – Driven DMA

Interrupt-driven DMA notifies the CPU via an interrupt request (IRQ) when a transfer is complete. The CPU pauses its current task to handle the interrupt.

This method reduces CPU overhead, allowing it to handle other tasks while waiting for data transfers. It also minimizes latency by immediately signaling the CPU upon completion, making it ideal for time-sensitive applications. Additionally, by decoupling the CPU from data transfers, it enhances multitasking and system flexibility.

However, interrupt-driven DMA requires hardware support for interrupt handling, increasing system complexity and cost. In systems with multiple interrupts, priority inversion may occur if a low-priority DMA transfer delays higher-priority tasks. Handling interrupts also incurs overhead from context switching, which can affect performance in high-frequency scenarios.

Comparison

When comparing the two methods, interrupt-driven DMA generally offers better performance and responsiveness compared to polling-based DMA, especially in systems with high data transfer rates or stringent latency requirements. Polling-based DMA is simpler to implement but may not be suitable for high-performance or real-time systems. Interrupt-driven DMA, while more complex, offers greater flexibility and efficiency.

In terms of resource utilization, polling-based DMA ties up the CPU, leading to inefficient resource utilization, whereas interrupt-driven DMA allows the CPU to perform other tasks concurrently, improving overall system efficiency.

Both polling-based DMA and interrupt-driven DMA have their advantages and disadvantages, making them suitable for different use cases. The choice between these methods depends on the specific requirements of the system and the trade-offs between simplicity, performance, and flexibility.

Buffer Size

The buffer size plays a crucial role in DMA (Direct Memory Access) transfers as it directly impacts the efficiency, performance, and resource utilization of the system. The buffer size determines the amount of data that can be transferred in each DMA operation before the CPU is involved. 

A larger buffer size allows for fewer DMA transactions, reducing the overhead associated with DMA setup and teardown, and maximizing the throughput of the transfer. However, an excessively large buffer size can lead to wasted memory resources and increased latency if the DMA controller must wait for the buffer to fill before initiating a transfer. 

Conversely, a smaller buffer size may result in more frequent DMA transactions, potentially increasing CPU overhead and reducing overall system performance. Therefore, selecting an optimal buffer size is essential to achieve efficient data transfer, minimize latency, and maximize system throughput in DMA-based applications.

In a research we performed <Link>, we found that each DMA method has its sweet spot before it stagnates or gets to the point of diminishing performance. The x-axis shows the buffer size and the y-axis shows the transfer rate in MB/sec. The transfers were tested for 10 seconds each.

DMA Performance Comparison Across DMA Methods and Types

Direct Memory Access is a critical technology for enhancing data transfer between devices and a computer’s main memory. This research examines the performance of various DMA implementations across multiple Linux kernel versions. By analyzing data transfer rates with different buffer sizes and DMA types (IOMMU, Direct, SMMU), we aim to identify the most efficient DMA configuration for this hardware setup. The findings provide insights into how these factors influence performance and contribute to a deeper understanding of DMA behavior across different kernels and buffer sizes.

While the specific results here apply to the tested devices and configurations, this research serves as an informative exploration of DMA performance. By studying how buffer size and DMA type affect transfer rates across Linux kernel versions, we hope to inspire further investigations into optimizing data transfer speeds on different hardware setups.

View our webinar on DMA 101: The Essential Webinar for Developers

Technical Specifications

Hardware:

  • Card: Bittware XUPP3R UltraScale+ card running an XDMA IP
  • X64 Machine: ASUS, Intel(R) Core(TM) i5-9400F CPU @ 2.90GHz, 24Gi RAM
  • ARM64 Machine: NVIDIA AGX Xavier, ARMv8 Processor rev 0 (v8l) 2.2GHz, 16Gi RAM

Operating system

  • X64: Ubuntu 18.04, Kernels 4.15, 5.3 / Ubuntu 22.04, Kernels 5.15, 6.0
  • ARM64: Ubuntu 20.04, Kernel 5.10

Disclaimer:

The results in this analysis are based on specific hardware and software configurations. Performance variations between different DMA types (IOMMU, Direct, SMMU) and buffer sizes depend on system configuration and Linux kernel versions. Since results may not be universally applicable, testing on the target hardware and software setup is recommended to determine the optimal DMA type and buffer size.

Methodology

  • Using the WinDriver XDMA sample (xdma_diag), sent buffers of increasing size to and from the device (Host to Card and Card to Host).
  • Measured the amount of information transferred in 10 seconds. 
  • Tested both Poll and Interrupt transfers. (Method)
  • Compared IOMMU DMA against Direct. (Type)

Main Conclusion

  • The IOMMU DMA method is consistently faster than the direct method.
  • The kernel version matters, newer kernel versions increase the transfer rate and DMA differences between methods. 
  • Larger buffer ≠ faster DMA. In most cases, there is a point of diminishing performance, or a plateau in transfer rate is reached. 
  • The ARM machine lagged behind the competition with a small buffer size but caught up as the buffer size was increased. 

**The X-axis represents the buffer size. The Y-axis represents the amount of data (in MB/Sec)  transferred in 10 seconds.  

Test 1 – C2H, Interrupt DMA method, MMU vs. Direct

  • The non-ARM DMAs reach a point of diminishing performance, where an increased buffer size does not improve the data transfer rate. 
  • The new IOMMU and SMMU designs outperform the original Direct design. In nearly all the tests, the amount of data transferred using IOMMU or SMMU is higher than using Direct for buffers of the same size. 
  • Newer kernel versions refine the difference between the methods. 

Device to Host Interrupts

Test 2 – C2H, Polling DMA method, MMU vs. Direct

  • In all DMA version types, the polling method transfers start at a much higher transfer rate than on the interrupts method.  
  • On older kernel versions (4.14 and 5.3) the transfers reach a plateau in transfer speed around a 1MB buffer. In newer kernel versions the IOMMU transfer rate significantly improves, but also reaches a point of diminishing performance.

Device to Host Polls

Test 3 – H2C, Interrupt DMA method, MMU vs. Direct

  • The transfer rate in the non-ARM DMA types starts from the same 64KB buffer size. On older kernel versions (4.14 and 5.3) the transfer rate remains relatively close as buffer size grows between all types. In newer kernel versions the IOMMU transfer rate significantly improves, alas maintaining the performance pattern.

Host to Device Interrupts

Test 4 – H2C, Polling DMA method, MMU vs. Direct

  • In all DMA version types, the polling method transfers start at a much higher transfer rate than on the interrupts method.  
  • The transfers reach a plateau in transfer speed around a 1MB buffer on all kernel versions. the IOMMU DMA type performance significantly improves In newer kernel versions.

Host to Device Polls

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Effective Drivers for FinTech Machine Learning Operations

The financial technology (FinTech) industry is undergoing a revolution driven by Machine Learning (ML).  ML algorithms, with their ability to analyze vast amounts of data and identify patterns, are transforming how financial services are delivered and consumed. 

The advantages of ML in FinTech are utilized to enhance security and fraud detection, increase efficiency, streamline processes, personalized financial services, and promote financial inclusion. ML has the potential to create a more efficient, inclusive, and user-centric financial landscape.

The technical foundation for utilizing ML in FinTech revolves around powerful hardware and robust data management. Data is the lifeblood of ML.  FinTech firms collect vast amounts of data on transactions, customer behavior, and market trends. 

Training complex ML models requires significant computing power.  Financial institutions rely on high-performance workstations or servers equipped with multiple GPUs and CPUs to accelerate these computationally intensive tasks. GPUs excel at parallel processing, allowing them to tackle massive datasets and complex algorithms much faster than traditional CPUs.

GPUs and Beyond

Beyond the dominance of GPUs in the realm of PCIe-based acceleration for FinTech ML, other specialized hardware emerges to address specific requirements.  Field-Programmable Gate Arrays (FPGAs) offer unparalleled flexibility. These versatile chips can be configured to execute particular algorithms or functions used within ML models.  For certain applications, FPGAs may even surpass GPUs in raw processing speed due to their customizability. This makes them attractive for tackling highly specialized tasks within the FinTech domain.

Furthermore, advancements in networking technology have led to the development of Smart Network Interface Cards (NICs). These intelligent network adapters go beyond simply transferring data. They can alleviate the CPU’s burden by handling tasks like data encryption, decryption, and network protocol processing. This offloading is particularly advantageous for FinTech applications that rely on high-speed data transfer and real-time processing, ensuring a smooth flow of information crucial for accurate and timely ML operations. 

The combined capabilities of PCIe technology and these specialized cards empower FinTech institutions to harness the full potential of ML algorithms, expediting model development, data analysis, and ultimately, driving innovation in financial services.

The Crucial Role of Driver in FinTech

While the raw power of PCIe cards like GPUs and FPGAs is undeniable, unlocking their full potential for FinTech ML relies on another crucial element: effective drivers. These software components act as intermediaries, translating instructions from the operating system and ML applications to the hardware itself.  High-performance drivers optimized for specific workloads play a significant role in maximizing the performance gains offered by PCIe cards.

Effective drivers achieve this in several ways. Firstly, they ensure efficient communication between the CPU and the PCIe card. This minimizes latency (delays) in data transfer, allowing the card to receive instructions and send results as quickly as possible. Secondly, optimized drivers can fine-tune the internal workings of the card, allocating resources like memory and processing power to specific tasks within the ML application. This targeted allocation ensures the card operates at peak efficiency, maximizing its processing capabilities for the specific demands of FinTech ML workloads.

Furthermore, well-maintained drivers offer stability and reliability. Bugs or compatibility issues with drivers can introduce errors or crashes, disrupting the smooth operation of ML models. By providing a stable and efficient communication channel between the software and hardware, effective drivers become an essential cog in the machine, empowering FinTech institutions to fully leverage the performance enhancements offered by PCIe technology and unlock the true potential of ML for financial innovation.

Building Python Apps with Hardware Access

The world of programming relies on a concept called abstraction. Different programming languages provide various levels of abstraction, acting as a bridge between the programmer and the hardware. High-level languages like Python hide much of the machine’s complexity. This allows developers to focus on logic and functionality. Python is widely used for its ease and versatility. However, low-level languages offer advantages in specific situations.

Low-level languages sit closer to the hardware, providing fine-grained control over system resources like memory and processor registers. Assembly language, for instance, directly translates human-readable instructions into machine code, the language computers understand. While notoriously intricate, assembly grants programmers unmatched control over how the hardware executes tasks.  Other low-level languages like C and C++ offer a balance between control and readability, allowing programmers to interact with hardware at a more manageable level.

Enhancing Python with Hardware Interaction Through WinDriver

While Python excels in various domains like data science and web development, its higher-level abstraction comes at a cost. When it comes to kernel development, the heart of an operating system, or directly manipulating hardware components, Python isn’t the ideal choice. This is where the power of low-level languages shines.

One might ask, why complicate things by introducing a lower-level language when Python itself seems sufficient? Here’s where the potential of WinDriver Python libraries comes into play. They bridge the gap between the high-level world of Python and the low-level functionalities of the kernel. This allows developers to harness the performance benefits of kernel-level interactions for specific tasks within their Python applications, without abandoning the ease of use and extensive libraries Python offers.

Imagine a Python-based application for image processing. While it excels in image manipulation algorithms, the actual execution might be bottlenecked by the underlying hardware. By utilizing WinDriver, developers can interact with the graphics card’s hardware acceleration features directly from within their Python application. This offloads the computationally intensive tasks to the GPU, significantly improving the processing speed and overall responsiveness of the application.

Beyond Performance: Kernel Access for Enhanced 3D Printing Control

The possibilities extend beyond performance optimization. Kernel-level access through Python libraries unlocks functionalities not readily available in high-level languages. Let’s talk about another example. Consider a Python application for 3D printing. While there are existing libraries for basic control of 3D printers in Python, these might lack precise control over specific functionalities. Kernel-level access offers several advantages: 

  • Fine-tuned Printing Parameters: Low-level code allows for more granular control over printing parameters like nozzle temperature, extrusion speed, and filament feed rate. 
  • Real-time Printer Monitoring:  Kernel-level access grants the application the ability to monitor various sensor readings directly from the 3D printer, such as temperature readings and filament flow sensors
  • Customizable Print Profiles:  Developers can leverage WinDriver to create user-defined print profiles within the Python application. These profiles could contain specific control parameters for different materials or desired print qualities.

By integrating kernel-level functionalities with the user-friendly nature of Python, developers can create powerful 3D printing control and monitoring applications. These applications offer precise control over the printing process, real-time monitoring capabilities, and customizable print profiles, making them valuable tools for both hobbyists and professional 3D printing enthusiasts.

The Future of Integrated Software Development

In conclusion, while Python remains a dominant force in application development, strategically leveraging libraries like WinDriver’s opens doors to a new level of functionality and performance within Python-based applications. By integrating controlled sections of low-level code, developers can unlock the hidden potential of the kernel, enabling Python applications to interact with hardware more directly, perform complex tasks more efficiently, and ultimately deliver a more robust and feature-rich user experience. 

This hybrid approach signifies the future of software development – leveraging the strengths of different languages to create applications that are both powerful and user-friendly.

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Device and Driver INFs – A Comprehensive Guide

Operating systems are made to ease the interaction between humans and computers. However, it is not just about convenient file storage and a friendly user interface. One of the basic roles of an operating system is to protect the user from unauthorized and malicious software. In many ways, the kernel is the most sensitive level of the computer. That is why Windows created the INF mechanism. 

Hardware developers (for Windows environment) must be familiar with the INF and digital signature mechanisms, to provide a safe and easy installation. 

What are INFs?

These files are plain text files crucial for installing software and drivers in Windows operating systems. They act as blueprints, providing instructions and details about the installation process. It includes the driver information, installation instructions, and configuration settings. When a new hardware component is connected, like a printer or a sound card, Windows searches for the corresponding INF file to understand how to install the necessary drivers.

This standardized format ensures consistency across different manufacturers and devices, allowing Windows to understand and interpret the information easily. INF files also incorporate basic security measures. They can specify trusted sources for driver files, preventing the installation of potentially harmful software. They can also limit certain actions and modifications, enhancing system stability and integrity.

To authorize the manufacturer of the device INF files are often accompanied by digital signatures. These signatures act like electronic seals, created using cryptographic algorithms and linked to a trusted entity. When Windows encounters an INF file, it verifies the digital signature using the public key of the trusted entity. If the signature is valid, it indicates that the file hasn’t been modified or tampered with since it was signed, ensuring its authenticity and trustworthiness.

Installing INFs During Installation
Installing INFs for Distribution

Driver and Device INFs

As we understand by now, on Windows, no access to the kernel will be possible without a trusted INF. WinDriver, offering a user-mode driver development toolkit, must gain access to the kernel to perform its magic.

Driver INF provides a bridge between the user and kernel mode. The default driver INF is WinDriver’s named windrvrVVVV (where VVVV=WinDriver’s version). Distributed machines do not have WinDriver installed, so in the case of distribution, your project should have both the device and driver INF installed. Both of them are automatically generated as part of your project. 

Device INF is unique to a particular device, enabling access to it. The device INF can be generated for development and testing purposes only, bound to the default INF, or as part of the distribution package. Both must be installed and enabled for the driver to work properly. Most of the Invalid Handle and device access issues are related to INF issues.

WDREG is another tool developed by WinDriver and is aimed to ease the driver development process. It is a dynamic kernel module loader, allowing you to install and enable (and vice versa) INFs. 

Learn more and experience it yourself in these links.

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Scatter-Gather or Contiguous? Understanding DMA Types

In PCIe driver development, DMA (Direct Memory Access) facilitates data transfer between a PCIe device and system memory without involving the CPU. This transfer method significantly improves performance compared to CPU-mediated transfers, as it frees up the CPU for other tasks.

The basic DMA architecture includes 4 steps, defined and managed in the driver:

  1. Allocating a memory region accessible for DMA – This means the region (in the memory) that has specific properties that allow the device to directly read and write from it.
  2. Providing the physical address of the memory region to the device – The device translates this address into a format it understands for its DMA operations.
  3. Initiating the DMA transfer – The device uses its DMA controller to directly transfer data between its internal memory and the allocated memory region on the host system.
  4. Notifying when the transfer is complete – Freeing the allocated space, and processing the data or triggers further actions.

The memory allocation depends on various factors; the type of data transferred, device-memory compatibility, system architecture, and more. The actual free memory that can be allocated for the transfer is perhaps the most critical factor. Sometimes, there is not enough contiguous space to allocate to the DMA. 

Both contiguous and scatter-gather DMA (also: SG) are techniques used in PCIe driver development to efficiently transfer data between a device and system memory, but they differ in how they handle buffer allocation:

Contiguous DMA  – Pages a single segment of the memory, locking it to execute the transfer. Managing the transfer on the device side is easier, but it’s not always possible if sufficient contiguous memory isn’t allocated. This constraint is generally due to fragmentation.

Scatter-Gather DMA – Allocates multiple, potentially non-contiguous blocks of memory. The data fragments residing in memory are managed with a descriptor table, telling the device which data should go where. It is more complex to set up on the driver side as you need to manage the descriptor table.

So, which one is better?

As said, contiguous DMA is easier to program, debug and manage. However, SG DMA can be more effective when the requirements demand data scattered across multiple memory locations, in cases of large data transfers and when contiguous memory is scarce.

Regarding performance and speed, there is no straightforward answer. It depends on the device (and which DMA types the hardware supports), the memory (data patterns and available space) and the driver complexity. We recommend checking different methods, including DMA combined with polling and interrupts. Sometimes the results can be surprising. 

WinDriver, the leading cross-platform driver development toolkit for PCI/PCIe devices offers several tools making the development of both types of DMA easier. As a developer, you will be able to perform kernel operations (including DMA-related) from the user mode. Moreover, we offer an automated generated code based on settings defined in a GUI application (no-code). If you are new to this operation, WinDriver includes free samples of DMA run and tested for industry standard DMA IP Cores from companies such as Intel Altera, AMD Xilinx, Lattice, and more.

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Mastering Driver Development for High-Speed PCIe Devices

Nobody likes latency. The need for high speed and precise data transfer arises everywhere, from satellites orbiting space to data centers deep beneath the Earth’s surface. PCIe 7.0 is capable of transferring 128 GT/s, fulfilling the promise to double the I/O bandwidth every 3 years. 

One of the kernel developer’s goals is to utilize the hardware fully. It is not just about making everything work, but also maximizing the product’s potential. 

Optimize PCIe Devices: 4 Key Areas to Address

  • Use DMA – Perhaps the first thing we think of when it comes to data transfer speed is Direct Memory Access.
    DMA allows for direct data transfer between the device and memory, bypassing the CPU. This cuts down on latency and boosts throughput. Although it can be more challenging to set up a DMA mechanism, a properly implemented DMA significantly improves performance and reduces latency.
  • Implement Kernel Plugin – Kernel plugins are chunks of code (performing specific operations) that sit only in the kernel mode. The fact that they run without constantly context-switching with the user mode, makes their loading dynamic and operations faster. This not only affects the speed, but also demands less resources.
  • Optimize your code – A fine code can make all the difference, especially when there are a lot of “moving parts”. Coding an effective interrupt handler, managing multiple queues, optimizing memory access patterns, and other critical components can also enhance the device’s speed.
  • Update your OS version – The benefits of working with the latest version of an operating system do not stop at performance improvement. New versions often include critical security and other bug fixes, and better compatibility, support, and APIs. Research showed that newer versions enhance device performance.

Efficient data transfer with minimal latency is crucial across various applications. Kernel developers aiming to fully utilize their device potential must consider performance enhancement strategies like DMA, implementing Kernel Plugins, optimizing code, and working with an updated operating system. Ultimately contributing to enhanced device performance.

WinDriver, the most reliable driver development toolkit serving leading companies for more than 20 years, offers DMA, kernel plugins, and handlers API. Not just that, WinDriver comes with a sample folder, including tested interrupt handlers, DMA, and kernel plugin, and code samples for DMA IPs by AMD/Xilinx, Intel/Altera, and Lattice among others. Regarding coding, WinDriver offers no-code features, including generating code for I/O operations and predefined actions set in the DriverWizard. 

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Effective Management of Device-Drivers Development Team

Bringing a device to life is no walk in the park. It’s the culmination of a rigorous journey through intricate challenges, and even then, the work doesn’t stop there. Throughout its lifespan, a device needs constant attention and evolution.

Somehow the physical material, electric circuits, chips, and firmware should perform specific tasks deterministically on a well-established operating system. It’s not only about the team’s expertise but also about the synergy of their collaborative efforts. 

Here are a few points to take into account when building and planning an effective device-drivers development team:

Common Language – Computers have compilers/interpreters to break down meanings. We humans, however, have a much wider context. Functions, logs, and calls coherency are key and can be achieved only with elaborate communication via recorded meetings, reviews, or (good) written documentation. Moreover, in large companies and growing teams, effective documentation can save valuable onboarding and hand-off time. 

Coherent Architecture – With sophistication comes complexity. Rooting for a coherent architecture is easier said than done. However, it shouldn’t stop us from striving to and pointing out the importance of it. An easy-to-grasp system takes less effort to get everyone on the same page when starting from scratch. In case of continuous development, or across different teams, the knowledge transfer can be quicker. 

Multi-Scale Compatibility – the product development and distribution process should be aligned with ever-changing environments; IDEs, kernels and operating systems, debugging tools, and other dependencies. Both people and toolkits should allow cross-platform scalability, during the whole product lifetime. Platforms and version adaptability will ensure your product’s relevancy for the latest platforms, and more importantly (security-wise), the ones that are still supported. 

WinDriver, the driver development toolkit by Jungo, offers a range of tools to bridge development gaps, ideal for an effective device-drivers development team. It comes with a built-in API, allowing developers to perform kernel operations from the user-mode, which simplifies the code, the debugging, and the hand-off. WinDriver supports all PCIe/USB devices and is cross-platform compatible for Windows/Linux/Linux ARM/MacOS, both for development and distribution. Every function and feature is widely documented, alongside its growing content center.

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© Jungo Connectivity Ltd. 2024 All Rights Reserved

IMPORTANT – READ CAREFULLY: THIS SOFTWARE LICENSE AGREEMENT (“AGREEMENT”) IS A LEGAL AGREEMENT BETWEEN YOU AND JUNGO CONNECTIVITY LTD. (“JUNGO”), FOR THE WINDRIVER SOFTWARE PRODUCT ACCOMPANYING THIS LICENSE (THE “SOFTWARE”). BY INSTALLING, COPYING OR OTHERWISE USING THE SOFTWARE, YOU AGREE TO BE LEGALLY BOUND BY THE TERMS AND CONDITIONS OF THIS AGREEMENT. IF YOU DO NOT AGREE TO THE TERMS AND CONDITIONS OF THIS AGREEMENT, DO NOT INSTALL, COPY, OR OTHERWISE USE THE SOFTWARE.

1.  OWNERSHIP OF THE SOFTWARE. All right, title, and interest in and to the Software, including associated intellectual property rights, of any sort and/or kind, are and shall remain solely with Jungo and its licensors, and may be protected by copyright, trademark, patent and trade secret law and international treaties. This Agreement does not convey to you an interest in or to the Software, but only a limited, non-transferable, non-sublicensable, non-exclusive, and revocable right of use, in accordance with the terms of this Agreement. You may not remove any proprietary notices and/or any legends from the Software, in whole or in part.

2.  GRANT OF LICENSE. Jungo hereby grants you a personal, non-exclusive, nontransferable, and non-sublicensable node-locked and time limited license to use the Software.

Individuals: Jungo grants you, as an individual, a personal, non-exclusive, “single-user” license to use the Software on a single computer, in the manner provided below, at the site for which the license was given.

Entities: If you are an entity, Jungo grants you the right to designate one individual within your organization (and only one) to have the right to use the Software on a single computer, in the manner provided below, at the site for which the license was given.

License Scope: A single user license allows usage of WinDriver and redistribution of certain components (as defined below) within a single end product SKU, for a single device (identified by its VID/PID (USB) or VID/DID (PCI)), and without SDK/API capabilities.  If you need extended license or distribution rights, please contact Jungo.

3.  EVALUATION LICENSE. If you have not yet paid license fees for the use of the Software, then Jungo hereby grants you a personal, non-exclusive, non-transferable and non-sublicensable license to internally use the Software for evaluation purposes only, for a period of 30 days (the “Evaluation License”). If, after the expiration of the Evaluation License, you wish to continue using the Software and accompanying written materials, you may do so by remitting the required payment to Jungo, and you will then receive a registration code and a license string that will permit you to use the Software on a single computer under one of the license schemes specified in Section 2 above.

4.  OPEN SOURCE. The Software includes certain files that are subject to open source licenses. These files are identified in their header files (“Open Source Files”). You must use the Open Source Files in accordance with the terms of their respective licenses.
In the event of any contradiction between the terms of this Agreement, and the terms of the open source license accompanying a certain Open Source File, the terms of the latter shall prevail, with regard to the said Open Source File.

RESTRICTIONS ON USE AND TRANSFER

5.  Distribution of Files:

(a) You may not distribute, or otherwise transfer or assign, any portion of the Software, including any of the headers or source files that are included in the Software, unless otherwise expressly permitted in this Agreement, subject to the provisions of Section 4 above.

(b) Subject to your full and continued compliance with the terms of this Agreement, including the ongoing payment of annual license fees, you may distribute the following files:

Windows:
windrvr1630.sys
windrvr1630.inf
wd1630.cat
wdapi1630.dll
wdapi_dotnet1630.dll
wdapi_java1630.dll wdapi_java1600.jar
wdreg.exe
difxapi.dll

Windows_CE:
windrvr1220.dll
wdapi1220.dll

Linux:
windrvr_gcc_v2.a windrvr_gcc_v3.a windrvr_gcc_v3_regparm.a
kp_linux_gcc_v2.o kp_linux_gcc_v3.o kp_linux_gcc_v3_regparm.o
libwdapi1630.so
libwdapi_java1630.so wdapi_java1600.java
kp_wdapi1630_gcc_v2.a kp_wdapi1600_gcc_v3.a kp_wdapi1600_gcc_v3_regparm.a
linux_wrappers.c linux_wrappers.h wdusb_linux.c
wdusb_interface.h wd_ver.h linux_common.h windrvr.h windrvr_usb.h
wdreg
configure makefile.in
configure.wd makefile.wd.in makefile.wd.kbuild.in
configure.usb makefile.usb.in makefile.usb.kbuild.in
setup_inst_dir

(c) The files listed in Section 5.b above may be distributed only as part
of a complete application that you distribute under your organization name,
and only if they significantly contribute to the functionality of your
application. For avoidance of doubt, each organization distributing these
files as part of the organization products is required to have valid
license(s) under the organization name/VID, irrespective of the party who
actually performed the product development. Licenses granted to
subcontractors do not grant distribution or other rights to the
organizations for which they are developing.

(d) The distribution of the windrvr.h header file is permitted only on Linux.

(e) You may not modify the distributed files specified in Section 5.b of this Agreement.

(f) You may not distribute any header file that describes the WinDriver functions, or functions that call the WinDriver functions and have the same basic functionality as that of the WinDriver functions.

6.  The Software may not be used to develop a development product, an API, or any products, which will eventually be part of a development product or environment, without the written consent of Jungo and subject to additional fees and licensing terms.

7.  You may make printed copies of the written materials accompanying the Software, provided that only users bound by this license use them.

8.  You may not allow any third party to use the Software, grant access to the Software (or any portion thereof) to any third party, or otherwise make any commercial use of the Software, including without limitation, assign, distribute, sublicense, transfer, pledge, lease, rent, or share your rights in the Software or any of your rights under this Agreement, all whether or not for any consideration.

9.  You may not translate, reverse engineer, decompile, disassemble, reproduce, duplicate, copy, or otherwise disseminate all or any part of the Software, or extract source code from the object code of the Software.

10. Jungo reserves the right to revise, update, change, modify, add to, supplement, or delete any and all terms of this License Agreement; provided, however, that changes to this License Agreement will not be applied retroactively. Such changes will be effective with or without
prior notice to you. You can review the most current version of this License Agreement under the WinDriver download form page.

11.  You may not incorporate or link any open source software with any open source software part of the Software, or otherwise take any action which may cause the Software or any portion thereof to be subjected to the terms of the Free Software Foundation’s General Public License (GPL) or Lesser General Public License (LGPL), or of any other open source code license.

12.  DISCLAIMER OF WARRANTY. THIS SOFTWARE AND ITS ACCOMPANYING WRITTEN
MATERIALS ARE PROVIDED BY JUNGO “AS IS” WITHOUT ANY WARRANTY. ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT, ARE HEREBY DISCLAIMED TO THE FULLEST EXTENT PERMITTED UNDER APPLICABLE LAW.

13.  NO LIABILITY. TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, IN NO
EVENT SHALL JUNGO OR ITS LICENSORS BE LIABLE FOR ANY DIRECT, INDIRECT,
INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, SAVINGS, IP INFRINGEMENT OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

14.  Governing Law. This Agreement and use of the Software are governed by the
laws of the State of Israel, regardless of its conflict of laws rules, and the
competent courts of the State of Israel shall have sole and exclusive
jurisdiction over any dispute under this Agreement or otherwise related to the
Software.

15.  Confidentiality. The Software, including any additional information
related thereto, contains confidential and proprietary information of Jungo.
Accordingly, you agree that you will not, nor allow any third party to,
disseminate, transfer, grant access to, or otherwise disclose to any third
party the Software or any part thereof or any other confidential or proprietary
information of Jungo provided in connection therewith. You will maintain all
copies of the Software and all related documentation in confidence.

16.  Termination and Effects of Termination. Jungo may terminate this Agreement
and the licenses granted to you hereunder at any time if you breach any of your
obligations hereunder, by issuance of written notice to such effect, addressed
to you at the address you provided in your registration form. Upon expiration
or other termination of this Agreement, the Licenses granted to you hereunder
shall immediately and automatically be canceled, and you will immediately
remove all copies of the Software from your computer(s) and cease any use
thereof.

17.  Contact Details. If you have any questions concerning this Agreement or wish to contact Jungo for any reason —

Web site: www.jungo.com
Email:    [email protected]

18.  U.S. GOVERNMENT RESTRICTED RIGHTS. The Software and documentation are provided with RESTRICTED RIGHTS.
Use, duplication, or disclosure by the Government is subject to restrictions set forth in subparagraph (c)(1) of The Rights in Technical Data and Computer Software clause at DFARS 252.227-7013 or subparagraphs (c)(1)(ii) and (2) of Commercial Computer Software – Restricted Rights at 48 CFR 52.227-19, as applicable.

19. Automatic Renewal. The subscription shall be automatically renewed, unless Licensee notifies Licensor 30 days or more prior to the expiration date of the subscription, of its intent not to renew the subscription.

 

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