When the option of hardware acceleration is allowed, it is always a wonderful idea to use it: usually, the application (or part of it) runs faster and, at the same time, using less energy. Moreover, the CPU will be free to process something else!!
Unfortunately, hardware acceleration doesn’t always work as smoothly as it should. The first time I recall encountering the option was when I disabled it in Chrome, because it was seemingly making my browser run much less stably. Here’s the cases where you should probably disable hardware acceleration:
- If your CPU is really strong and your other components are really weak, acceleration may actually be ineffective in comparison to just letting the powerhouse take care of things. Additionally, if your components are prone to overheating/are damaged in any way, intensive use through hardware acceleration may be causing problems you wouldn’t experience otherwise.
- The software designed to utilize the hardware isn’t doing it well or can’t run as stably as it does when using only the CPU. This is a common reason to disable hardware acceleration in an app’s options, unfortunately, but it does happen.
Hardware acceleration combines the flexibility of general-purpose processors, such as CPUs, with the efficiency of fully customized hardware, such as GPUs and ASICs, increasing efficiency by orders of magnitude when any application is implemented higher up the hierarchy of digital computing systems. For example, visualization processes may be offloaded onto a graphics card in order to enable faster, higher-quality playback of videos and games, while also freeing up the CPU to perform other tasks.
There is a wide variety of dedicated hardware acceleration systems. One popular form is tethering hardware acceleration, which, when acting as a WiFi hotspot, will offload operations involving tethering onto a WiFi chip, reducing system workload and increasing energy efficiency. Hardware graphics acceleration, also known as GPU rendering, works server-side using buffer caching and modern graphics APIs to deliver interactive visualizations of high-cardinality data. AI hardware acceleration is designed for such applications as artificial neural networks, machine vision, and machine learning hardware acceleration, often found in the fields of robotics and the Internet of Things.
Systems often provide the option to enable or disable hardware acceleration. For instance, hardware acceleration is enabled by default in Google Chrome, but this capability can be turned off or relaunched in the system settings under “use hardware acceleration when available.” In order to determine if hardware acceleration is working properly, developers may perform a browser hardware acceleration test, which will detect any compatibility issues.
The most common hardware used for acceleration include:
- Graphics Processing Units (GPUs): originally designed for handling the motion of image, GPUs are now used for calculations involving massive amounts of data, accelerating portions of an application while the rest continues to run on the CPU. The massive parallelism of modern GPUs allows users to process billions of records instantly.
- Field Programmable Gate Arrays (FPGAs): a hardware description language (HDL)-specified semiconductor integrated circuit designed to allow the user to configure a large majority of the electrical functionality. FPGAs can be used to accelerate parts of an algorithm, sharing part of the computation between the FPGA and a general-purpose processor.
- Application-Specific Integrated Circuits (ASICs): an integrated circuit customized specifically for a particular purpose or application, improving overall speed as it focuses solely on performing its one function. Maximum complexity in modern ASICs has grown to over 100 million logic gates.