In our previous article, “Key points about DevOps” we talked about what DevOps is and how it can help you optimize key elements in your company, such as shortening launch times, accelerating the realization of new products and services, and reducing costs. In this article, we will learn more about some good practices to start implementing this as soon as possible.
Doing a brief review, DevOps refers to the words ‘Development’ and ‘Operations’, it is a discipline that, despite being relatively recent, has allowed many companies and organizations to rethink their processes for more efficient and agile ones, managing to increase its competitiveness and efficiency.
Good practices on DevOps
For sure, there are countless benefits to the implementation of this methodology, taking into account that it does not imply an improvement in terms of technology or productivity in itself, but rather it allows to streamline the level of communication and collaboration between departments for optimal execution of operations, time and quality of delivery.
Adopting this new process does not happen overnight, and expected results could be negatively impacted if the company implements ineffectively.
At Huenei, we use this methodology to increase the effectiveness of our software development teams, as well as to improve the quality of the constant deliveries in Custom Software, Mobile Applications, involving it in Testing & QA and UX / UI processes. So we recommend following these practices:
1 – Continuous Integration Process (CPI)
This process is strongly supported by agile methodologies, its principle is to reduce implementation and deployment times, managing to divide a project into more parts, making gradual deliveries.
How this could help? As the team of developers constantly review changes in the repository code, they can more quickly detect any type of flaw and even constantly improve the execution of the operation. It is these early findings in the software life cycle that will help the development department to solve problems almost on the spot (and even prevent them).
2 – Continuous delivery
Arguably, this step accompanies the previous one, as starting with development, build, unit testing, static code analysis, and static analysis security testing in the PCI, it promotes automation of functional tests, integration, performance, and security, along with configuration management and deployment.
As the latter are critical areas for automation, and essential in a DevOps context, it is a type of practice that increases the amount of testing and verification at various stages in manual and automated code cycles.
In addition to allowing a team to build, test and launch the codebase in a faster and more frequent way, which by dividing it into cycles of smaller size and duration, speeds up the processes of organizations, allowing them to carry out more launches, reduce manual deployments and reduce the risks of production failures.
3 – Communication management
A key element in DevOps management, since its focus is on keeping all interested parties related to development, operation, and implementation informed, taking into account that we are integrating the tasks of different departments. Therefore, communication is essential and becomes a fundamental element for a total adoption, keeping everyone on the same page to involve them in the whole process, keep up to date with the organization and avoid doubts between them or the final products.
To apply the strategy correctly, it is vital to keep all the teams and members up to date, in this way it is guaranteed that the leaders of the organization (from the sales, production, and management departments) can be involved in the processes and guide the team development to make successful changes, from their perspectives and knowledge.
4 – Test automation
In software development, regular testing is essential to create quality code, which is vital to implement in a DevOps management, not only because it saves time by quickly completing tedious and time-consuming tasks, but also because it allows you to identify quickly early failures in pre-deployment processes.
This in itself is the core of agility and innovation, since the additional time that team members will have can be invested in tasks with greater added value or in monitoring the results of new processes, identifying failures and opportunities for improvement.
5 – Continuous monitoring
Like all cultural and work process changes, this requires close and continuous monitoring to anticipate and identify that all actions are being carried out correctly and that performance is as expected.
Continuous delivery of feedback from all members of the organization in real-time is vital to the organization; from the production team that runs the application to the end customer at the official launch stage. In this way, we ensure that the developer can benefit from all the valuable feedback on the end-user experience in the process and in turn, modify the code to meet the expectations of the end-user.
With an ever-evolving business environment and an ever-improving and growing technology landscape, organizations must seek to stay ahead, with DevOps they can increase the speed and quality of software implementations by improving communication and collaboration between parties. interested.
In themselves, these good practices allow creating a clear guide to guide any company, of any size and industry, to immerse themselves in the necessary cultural change, managing to increase productivity and efficiency through high-quality deliveries, adding transparency to their processes. and open collaboration across development and operations teams.
Know more about our process in the Software Development section.
In the previous articles, we talked about Hardware Acceleration with FPGAs, the Key concepts about acceleration with FPGA that they provide, and the Hardware acceleration applications with FPGAs. In this latest installment of the series, we will focus on Hardware Accelerated Libraries and Frameworks with FPGAs, which implies zero changes to the code of an application. We will review the different alternatives available, for Machine and Deep Learning applications, Image, and Video Processing, as well as Databases.
Options for development with FPGAs
Historically, working with FPGAs has always been associated with the need for a Hardware developer, mainly Electronic Engineers, and the use of tools and Hardware Description Languages (HDL), such as VHDL and Verilog (of the concurrent type in instead of sequential), very different from those used in the field of Software development. In recent years, a new type of application has appeared, acceleration in data centers, which aims to reduce the gap between the Hardware and Software domains, for the cases of computationally demanding algorithms, with the processing of large volumes of data.
Applying levels of abstraction, replacing the typical HDL with a subset of C / C ++ combined with OpenCL, took the development to a more familiar environment for a Software developer. Thus, basic blocks (primitives) are provided, for Mathematical, Statistical, Linear Algebra, and Digital Signal Processing (DSP) applications. However, this alternative still requires a deep knowledge of the hardware involved, to achieve significant accelerations and higher performance.
Secondly, there are accelerated libraries of specific domains, for solutions in Finance, Databases, Image, and Video Processing, Data Compression, Security, etc. They are of the plug-and-play type and can be invoked directly with an API from our applications, written in C / C ++ or Python, requiring the replacement of “common” libraries with accelerated versions.
Finally, we will describe the main ones in this article, there are open source libraries and frameworks, which were accelerated by third parties. This allows us, generally running one or more Docker instances (on-premise or in the cloud), to accelerate Machine Learning applications, Image processing, and Databases, among others, without the need to change the code of our application.
Without a doubt, one of the most disruptive technological advances in recent years has been Machine Learning. Hardware acceleration brings many benefits, due to the high level of parallelism and the enormous number of matrix operations required. They are seen both in the training phase of the model (reducing times from days to hours or minutes) and in the inference phase, enabling real-time applications.
Here is a small list of the accelerated options available:
TensorFlow is a platform for building and training neural networks, using graphs. Created by Google, it is one of the leading Deep Learning frameworks.
Keras is a high-level API for neural networks written in Python. It works alone or as an interface to frameworks such as TensorFlow (with whom it is usually used) or Theano. It was developed to facilitate a quick experimentation process, it provides a very smooth learning curve.
PyTorch is a Python library designed to perform numerical calculations via tension programming. Mainly focused on the development of neural networks.
Deep Learning Framework noted for its scalability, modularity and high-speed data processing.
Scikit-learn is a library for math, science, and engineering. Includes modules for statistics, optimization, integrals, linear algebra, signal and image processing, and much more. Rely on Numpy, for fast handling of N-dimensional matrices.
XGBoost (Extreme Gradient Boosting), is one of the most used ML libraries, very efficient, flexible and portable.
Spark MLlib is Apache Spark’s ML library, with scaled and parallelized algorithms, taking advantage of the power of Spark. It includes the most common ML algorithms: Classification, Regression, Clustering, Collaborative Filters, Dimension Reduction, Decision Trees, and Recommendation. It can batch and stream. It also allows you to build, evaluate, and tune ML Pipelines.
Image and Video Processing
Image and Video Processing is another of the areas most benefited from hardware acceleration, making it possible to work in real-time on tasks such as video transcoding, live streaming, and image processing. Combined with Deep Learning, it is widely used in applications such as medical diagnostics, facial recognition, autonomous vehicles, smart stores, etc.
The most important library for Computer Vision and Image and Video Processing is OpenCV, open source, with more than 2500 functions available. There is an accelerated version of its main methods, adding more version after version.
For Video Processing, in tasks such as Transcoding, Encoding, Decoding and filtering, FFmpeg is one of the most used tools. There are accelerated plugins, for example for decoding and encoding H.264 and other formats. In addition, it supports the development of its own accelerated plugins.
Databases and analytics
Databases and Analytics receive increasingly complex workloads, mainly due to advances in Machine Learning, which forces an evolution of the Data Center. Hardware acceleration provides solutions to computing (for example with database engines that work at least 3 times faster) and storage (via SSD disks that incorporate FPGAs between their circuits, with direct access to data processing). the data). Some of the Accelerated Databases, or in the process of being so, mainly Open Source both SQL and NoSQL, are PostgreSQL, Mysql, Cassandra, and MongoDB.
In these cases, generally what is accelerated are the more complex low-level algorithms, such as data compression, compaction, aspects related to networking, storage, and integration with the storage medium. The accelerations reported are in the order of 3 to 10 times faster, which compared to improvements of up to 1500 times in ML algorithms may seem little, but they are very important for the reduction of costs associated with the data center.
Throughout this series of 4 articles, we learned what a device-level FPGA is, how acceleration is achieved when we are in the presence of a possible case that takes advantage of them (computationally complex algorithms, with large volumes of data). General cases of your application and particular solutions, ready to use without code changes.
How can Huenei help your business with Hardware Acceleration with FPGAs?
Infrastructure: Definition, acquisition and start-up (Cloud & On-promise).
Consulting: Consulting and deployment of available frameworks, to obtain acceleration without changes in the code.
Development: Adaptation of existing software through the use of accelerated libraries, to increase its performance.
We previously learned about the benefits of Hardware Acceleration with FPGAs, as well as a few Key concepts about acceleration with FPGAs for companies and teams. In this article, we will learn about the applications that typically benefit from employing this technology.
First, we will compare distributed and heterogeneous computing, highlighting the role of FPGAs in data centers. Then, we will present the most widespread uses of Acceleration with FPGAs, such as Machine Learning, Image and Video Processing, Databases, etc.
Distributed vs. Heterogeneous Computing
Over the past 10 years, we have witnessed an exponential growth in the generation of data. This in part thanks to the rise and popularity of electronic devices, such as cell phones, Internet of Things (IoT), wearable devices (smart-watches), among many others.
At the same time, the consumption of higher quality content by users has been increasing, a clear example being the case of television and/or streaming services, which have gradually increased the quality of content, resulting in a greater demand for data.
This growth in the creation/consumption of data brought the appearance of new computationally demanding applications, capable of both leveraging data and aiding in their processing.
However, an issue can arise with processing times, which directly affects the user experience, making the solution impractical. This raises the question: how can we reduce execution times to make the proposed solutions more viable?
One of the solutions consists of using Distributed Computing, where more than one computer is interconnected to a network to distribute the workload. Under this model, the maximum theoretical acceleration to be obtained is equal to the number of machines added in the data processing. Although it is a viable solution, it offers the problem that the time involved in distributing and transmitting data over the network must be considered.
For example, if we want to reduce data processing time to one third, we would have to configure up to four computers, which would skyrocket energy costs and the necessary physical space.
Another alternative is to use Heterogeneous Computing. In addition to using processors (CPUs) for general purposes, this method seeks to improve the performance of one computer by adding specialized processing capabilities to perform particular tasks.
This is where general-purpose graphics cards (GPGPUs) or programmable logic cards (FPGAs) are used, one of the main differences being that the former have a fixed architecture, while the latter are fully adaptable to any workload, in addition to a smaller energy consumption (since they generate the exact Hardware to be used, among other reasons).
Unlike Distributed Computing, in Heterogeneous Computing, acceleration will depend on the type of application and the architecture developed. For example, in the case of databases, the acceleration may have a lower frequency than a Machine Learning inference case (which can be accelerated by hundreds of times); another example would be the case of financial algorithms, where the acceleration rate is given in the thousands.
Additionally, instead of adding computers, boards are simply added in PCIe slots, saving resources, storage capacity, and energy consumption. This results in a lower Total Cost of Ownership (TCO).
FPGA-based accelerator cards have become an excellent accesory for data centers, available both on-premise (own servers) and in cloud services like Amazon, Azure, and Nimbix, among others.
Applications that benefit from hardware acceleration with FPGAs
In principle, any application involving complex algorithms with large volumes of data, where processing time is long enough to mitigate access to the card, is a candidate for acceleration. Besides, the process must be carried out through parallelization. Some of the typical solutions for FPGAs which respond to these characteristics are:
One of the most disruptive techniques in recent years has been Machine Learning (ML). Hardware acceleration can bring many benefits due to the high level of parallelism and the huge number of matrix operations required. These can be seen both in the training phase of the model (reducing this time from days to hours or minutes) and in the inference phase, enabling the use of real-time applications, like fraud detection, real-time video recognition, voice recognition, etc.
Image and Video Processing is one of the areas most benefited by acceleration, making it possible to work in real-time on tasks such as video transcoding, live streaming, and image processing. It is used in applications such as medical diagnostics, facial recognition, autonomous vehicles, smart stores, augmented reality, etc.
Databases and Analytics receive increasingly complex workloads due to advances in ML, forcing an evolution of data centers. Hardware acceleration provides solutions to computing (for example, with accelerators that, without touching code, accelerate PostgreSQL between 5-50X or Apache Spark up to 30x) and storage (via smart SSDs with FPGAs).
The large volumes of data to be processed require faster and more efficient storage systems. By moving information processing (compression, encryption, indexing) as close as possible to where the data resides, bottlenecks are reduced, freeing up the processor and reducing system power requirements.
Something similar happens with Network Acceleration, where information processing (compression, encryption, filtering, packet inspection, switching, and virtual routing) moves to where the data enters or leaves the system.
High-Performance Computing (HPC) is the practice of adding more computing power, in such a way as to deliver much higher performance than a conventional PC, to solve major problems in science and engineering. It includes everything from Human Genome sequencing to climate modeling.
In the case of Financial Technology, time is key to reducing risks, making informed business decisions, and providing differentiated financial services. Processes such as modeling, negotiation, evaluation, risk management, among others, can be accelerated.
With hardware acceleration, Tools and Services can be offered to process information in real-time, helping automate designs and reducing development times.
Making a brief comparison between the models, in Distributed Computing, more than one computer is interconnected in a network and the workload is distributed among all of them. This model, used for example by Apache Spark, is highly scalable but has a high energy consumption and requires a large physical space, which increases proportionally.
As for Heterogeneous Computing, the performance of one computer is improved by adding hardware (for example, via graphics cards such as GPGPUs or FPGAs), adding specialized processing capabilities. This makes it possible to obtain acceleration rates that depend on the type of application, but that can be, in some cases, between 1-10X (for example, when using Databases) up to hundreds or thousands of times when using Machine Learning.
Through a profiling and validation analysis of the feasibility of parallelizing the different processes and solutions, we can determine if FPGA Hardware Acceleration is the ideal solution for your company, especially when working with complex algorithms and large data volumes.
In this way, your business can improve user satisfaction rates by offering a faster and smoother experience with reduced processing times; additionally, and thanks to the reduction of TCO, your budget control can be optimized.
What is DevOps?
The combination of “development” and “operations” known as DevOps has brought about a cultural change closing the gap between two major teams that had historically functioned separately. This relationship reflects a culture that fosters collaboration between both teams to automate and integrate processes between software development and IT teams in order to build, experiment and project software in the shortest possible time.
Thanks to this, the capacity of a company or institution to provide almost immediate applications and services is also increased, allowing them to better serve their clients and compete more efficiently in the market. The teams can use innovative practices to automate processes that were previously carried out manually, always relying on tools that help them manage and optimize applications faster.
DevOps can operate under a variety of models where even QA and Security teams can be integrated into the mix of development and operations teams, from development and testing to application deployment. This is especially common when security is a priority in a project.
How do I know if I need DevOps?
If your company or organization is going through any of these situations, it is quite likely that you will need to implement a dynamic DevOps:
- The development team is having trouble optimizing old code, creating new code, or preparing new product features.
- Disparities between development and production teams have caused errors and compatibility failures.
- The improvements that were implemented (related to the software deployment) are currently obsolete.
- Your company is experiencing a slow time-to-market, that is, the process that goes from code development to production is too slow and inefficient.
How to carry out a DevOps implementation process
Adopt a DevOps mindset
Identify areas where your company’s current delivery process is ineffective and needs to be optimized. This will be a great opportunity to bring about real change in your organization, so you must be open to experimentation. Learn from possible short-term failures, they will help you improve; do not get used to accepting inefficient dynamics solely based on “tradition”.
Take advantage of the metrics
It is important to choose the correct metrics to verify and monitor the project. Do not be afraid to measure what might not look good at first, since this is what will help you notice real progress, as well as real commercial benefits.
Accept that there is no single path
Each organization must go through different DevOps circumstances linked to its business and culture, where the way they should do things will depend more on how patterns and habits within teams can be changed than the actual tools used to enable automation.
Don’t leave QA aside
Companies that implement DevOps often focus on automating deployments, leaving QA needs aside. Although we know that not all tests can be automated, it is essential to automate at least the tests that are run as part of the continuous integration process, such as unit tests or static code analysis.
Learn to look at automation from a smarter perspective
You can’t really speed up delivery processes without automating. The infrastructure, environment, configuration, platform and tests are written in code. Therefore, if something begins to fail or becomes ineffective, it is probably time to automate it. This will bring various benefits such as reducing delivery times, increasing repetitions and eliminating drift from the configuration.
There are several reasons why DevOps is useful for development teams, below we will examine the most important ones:
- Due to its high predictability, the DevOps failure rate is quite low
- This dynamic allows you to reproduce everything, in case you want to restore the previous version without problems
- If the current system is disabled or some new version fails, DevOps offers a quick and easy recovery process
- Time-to-market reduced to 50% through optimized software delivery. Good news for digital and mobile applications
- Infrastructure issues are built into DevOps, so teams can deliver higher quality in app development
- Security is also incorporated into the software life cycle, also helping to reduce defects
- DevOps leads to a more secure, resilient, stable and 100% auditable change operating system
- Cost efficiency during development is possible when implementing DevOps tools and models
- It is based on agile programming methods, allowing it to divide the base of the largest code into smaller and more adaptable snippets
Before DevOps arrived, development teams and operations teams worked in isolation, resulting in much longer times than it typically takes for a real build. This model was quite inefficient as testing and implementation was carried out after design and build, plus manual code deployment gave way to multiple human errors in production, further slowing down any project.
With the implementation of DevOps, the speed of development has been increased resulting in fast deliveries and higher quality updates; security has also been increased, making it one of the most reliable systems. Today’s digital transformation has increased the demands for software development, and DevOps has shown that it can cover all needs quite well and meet market expectations.
In our previous article “Hardware Acceleration with FPGAs“, we learned about what programmable logic devices are called FPGAs (Field-Programmable Gate Array), discussed their use as a companion to CPUs to achieve this acceleration, and named some advantages of use versus GPUs (Graphics Processing Unit).
To get a better idea, we will delve into the technical aspects to understand how they generally work, from how acceleration is achieved or how technology is accessed, to some considerations for its use with a software solution.
The two main reasons why FPGAs have higher performance than CPUs would be the possibility of using custom hardware and its great parallelism power.
Let’s take as an example to perform a sum of 1,000 values, in the case of CPUs, which are made up of fixed general-purpose hardware, the process would begin with the storage of the values in RAM memory, then they pass to the internal memory of quick accesses (memory Cache) where they will be taken to load two registers.
After configuring the Logical Arithmetic Unit (ALU) to achieve the desired operation, a partial sum of these 1,000 values is performed and the result is stored in a third register. Then, a new value is taken, the partial result obtained in the previous point is loaded and the operation is performed again.
After the last iteration, the final result is saved in the Caché memory where the information will be accessible in case it is required later, being stored in the system RAM to be a consolidated value of the running program. In the case of an FPGA, the entire previous cycle is reduced to 1,000 registers, where their values are directly added at the same time.
We must bear in mind that, sometimes, the FPGA will have to read values from a system RAM memory, finding itself in a situation similar to that of the CPU. However, the advantage over CPUs is that it does not have 1,000 general-purpose registers that can be given the exclusive use of storing values for a sum. In contrast, a CPU has few registers available to be shared in performing different operations.
An example to understand the power and acceleration of FPGAs would be to visualize that we have a loop of five instructions in the CPU’s program memory, it takes data from memory, processes it and returns it. This execution would be sequential, an instruction per time-lapse, forcing them to get to the last one in order to start the process again.
In an FPGA, the equivalent of each of the CPU instructions can be executed in a parallel block, feeding each input with the previous output.
As we can see in the image, we see that the time elapsed until the first data is obtained is the same in both cases, however, the period in which 4 results are achieved in a CPU is achieved up to 16 in the FPGA.
Although it is a didactic example, keep in mind that, thanks to the average hardware with which FPGAs can be configured, the acceleration could be up to hundreds or thousands of times in a real implementation.
It should be noted that FPGAs used as accelerators that accompany a CPU has spread in recent years. On the one hand, thanks to the high transfer rates achieved by the PCI Express protocol (used on the PC to interconnect the two devices in question).
On the other, given the speeds and storage capacity offered by DDR memories. For what accelerating makes sense, the amount of data involved has to be such that it is worth the entire process of moving it to the accelerator. On the other hand, we must be in the presence of a complex mathematical algorithm, where each step requires the results of the previous step, capable of being divided and parallelizable.
The Hardware Needed for Acceleration
The two main manufacturers of FPGAs, Xilinx, and Intel, offer a variety of accelerator cards, called Alveo and PAC respectively, that connect to the PCI Express buses of a server.
When wanting to include them in our infrastructure, we must consider the specifications of the receiving server equipment, as well as the system configurations and licenses of the development software.
There are services, such as Amazon, that offer ready-to-use development images elastically, as well as instances of Xilinx hardware. Keep in mind that there are also other services, such as Microsoft Azure whose instances are based on Intel devices, or in the case of Nimbix, with support from both platforms, to name a few.
Accelerator development is a task associated with a circuit design that involves the use of a hardware description language (HDL), although you can alternatively High-Level Synthesis (HLS), a subset of the C / C ++ language. Finally, OpenCL can be used just like in developing GPU accelerators. Usually, this type of technology is binding on Electronic Engineering specialists such as programming experts.
Fortunately, both technology providers and third-parties offer ready-to-use accelerators for known and widely used algorithms. Accelerated software applications are written in C / C ++, but there are APIs available for other languages, such as Python, Java, or Scala.
In case you need to perform any additional optimization, you will need to convert C / C ++ applications on a client/server, create a plugin, or perform a binding. In addition, there are also frameworks and libraries ready to use without changes from the application, related to Machine Learning, image, and video processing, SQL and NoSQL databases, among others.
From Huenei, we can accompany you through the adoption of this technology. After analyzing your application, we can offer you the infrastructure that best suits your processes. One option is advice on the use of available frameworks, libraries, and accelerated solutions, which do not require changes to the source code.
Another alternative is refactoring using a special API with custom accelerators, or directly initiating developments with a view to using these solutions. In any case, you will have the guide of specialists who are up to date with the latest trends in this area, which are so necessary in order to face the challenges of data with exponential growth and the use of computationally complex algorithms.
The revolution derived from the rise and spread of computers posed a before and after, standing out in the dizzying
The root of this development was the born of the CPU. As we well know, these are general-purpose devices, designed to execute sequential code. However, in recent years, high computational cost applications emerged, which generated the need for specific hardware architectures.
One solution consisted of creating graphics processors (GPUs), which were introduced in the 1980s to free CPUs from demanding tasks, first with the handling of 2D pixels, and then with the rendering of 3D scenes, which involved a great increase in their computing power. The current architecture of hundreds of specialized parallel processors results in high efficiency for executing operations, which led to their use as general-purpose accelerators. However, if the problem to be solved is not perfectly adapted, development complexity increases, and performance decreases.
What are FPGAs?
Both CPUs and GPUs have fixed hardware architecture. The alternative with reconfigurable architecture, are the devices known as Field-programmable Gate Array (FPGA). They are made up of logical blocks (they solve simple functions and, optionally, save the result in a register) and a powerful interconnection matrix. They can be considered integrated blank circuits, with a great capacity for parallelism, which can be adapted to solve specific tasks in a performative way.
What are they for us?
The general concept behind the use of this technology is that a complex computationally demanding algorithm moves from an application running on the CPU to an accelerator implemented on the FPGA. When the application requires an accelerated task, the CPU transmits the data and continues with its tasks, the FPGA processes them and returns them for later use, freeing the CPU from the said task and executing it in less time.
The acceleration factor to obtain will depend on the algorithm, the amount and type of data. It can be expected from a few times to thousands, that in processes that take days to compute translates it down to hours or minutes. This is not only an improvement in the user experience but also a decrease in energy and infrastructure costs.
While any complex and demanding algorithm is potentially accelerable, there are typical use cases, such as:
Deep / Machine Learning and Artificial Intelligence in general
Predictive Analysis type applications (eg Advanced fraud detection), Classification (eg New customer segmentation, Automatic document classification), Recommendation Engines (eg Personalized Marketing), etc. Accelerated frameworks and libraries are available such as TensorFlow, Apache Spark ML, Keras, Scikit-learn, Caffe, and XG Boost.
Financial Model Analysis
Used in Banks, Fintech, Insurance, for example, to detect fraudulent transactions in real-time. Also for Risk Analysis (with CPU, banks can only perform risk models once a day, but with FPGAs, they can perform this analysis in real-time). In finance, algorithms such as the accelerated Monte Carlo are used, which estimates the variation over time of stock instruments.
Interpretation of Medical or Satellite Images, etc. with accelerated OpenCV algorithms.
Video processing in real-time: Used in all kinds of automotive applications, Retail (Analytics in Activity in Stores), Health, etc. using OpenCV accelerated algorithms and FFmpeg tools.
Real-time analysis of large volumes of data, for example, coming from IoT devices via Apache Spark.
Hybrid Data Centers, with different types of calculations for different types of tasks (CPU, GPU, FPGA, ASIC).
Cryptography, Compression, Audit of networks in real-time, etc.
Typically, accelerators are invoked from C / C ++ APIs, but languages such as Java, Python, Scala or R can also be used and distributed frameworks such as Apache Spark ML and Apache Mahout
Not all processes can be accelerated. Similar to what happens with GPUs, applying this technology erroneously entails slow execution, due to the penalty for moving data between devices.
In addition, it must be taken into account that a server infrastructure with higher requirements is required for its development and use. Finally, to achieve a high-quality final product, highly specialized know-how is needed, which is scarce in today’s market.
In recent years, FPGA technology has approached the world of software development thanks to accelerator boards and cloud services. The reasons for using them range from achieving times that are not otherwise met or improving the user experience, to reducing energy and accommodation costs.
Any software that involves complex algorithms and large amounts of data is a candidate for hardware acceleration. Some typical use cases include, but are not limited to, genomic research, financial model analysis, augmented reality and machine vision systems, big data processing, and computer network security.