Indeed, GPGPU (General-Purpose Computing on Graphics Processing Units) is Turing complete, meaning it can theoretically perform any computation that a CPU can, without any inherent limitations. However, when it comes to tasks like video encoding, GPUs do not rely solely on their general-purpose processing units (GPGPU); they instead incorporate dedicated circuitry (like NVENC for NVIDIA) specifically designed for video encoding. This dedicated circuitry is not Turing complete—it’s designed for specific, limited tasks, and operates within fixed modes for efficiency.
01
Why Not Use GPGPU for Everything?
Although a GPGPU can handle any computational task, it isn’t optimized for certain types of operations. Specifically, GPGPUs excel at parallel processing, such as handling large sets of data through operations like “a × b + c × d” in parallel. However, they are not as efficient at handling tasks involving conditional logic, such as “if a > b, then c + d.” This kind of conditional branching is more common in tasks like video encoding, where decisions must be made based on varying data patterns.
If you force the GPGPU to handle these types of tasks, performance drops significantly. In fact, for such tasks, the GPGPU may become slower than a similarly priced CPU. Moreover, don’t forget that a GPU consumes far more power when running at full capacity compared to a CPU, further diminishing its efficiency for non-parallel tasks like video encoding.
02
The Role of Dedicated Circuits in Video Encoding
Dedicated circuits, like video encoding hardware in GPUs, are designed to compensate for the GPGPU’s limitations in handling conditional logic. These specialized circuits are optimized for specific encoding tasks, allowing them to perform much faster than the GPGPU could for the same task. However, this compensation is not perfect. Even after leveraging specialized circuits, the encoding performance might not match that of a CPU in terms of flexibility or compression efficiency.
03
GPU Encoding: Speed vs. Compression Efficiency
Video encoding involves a trade-off between compression rate (how efficiently data is compressed) and processing speed. Higher compression rates require more sophisticated decision-making and complex algorithms, which heavily rely on conditional operations that are hard for a GPGPU to handle efficiently. That’s why GPUs focus on lower compression rates with higher speeds.
In practice, GPUs are designed to balance speed and efficiency by relying on specific encoding modes that are hardwired into the dedicated circuits. Users can adjust a limited set of parameters (like bitrate, resolution, etc.) to strike a balance between compression quality and encoding speed. However, because these hardware circuits are fixed in their capabilities, they offer only a limited range of adjustable parameters, unlike software encoding on a CPU, which offers much more flexibility through a wider range of settings.
04
Summary
The reason GPU encoding is often inferior to CPU encoding is not due to the floating-point precision of the GPU, but rather due to the GPU’s design philosophy. GPU encoding focuses on speed rather than the complex decision-making required for high-efficiency compression, which CPUs handle better. This design limitation is rooted in the GPU’s reduced capacity for handling conditional logic, not in its ability to perform floating-point calculations.
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