Differences between Vulkan and Vulkan Compute for GPGPU and graphics

When one starts tinkering with GPU Computing It's normal to get confused with so many APIs: OpenGL, Vulkan, DirectX, CUDA… and, within Vulkan, to hear people talk about “Vulkan Compute” as if it were something different. If you're thinking about implementing simulations, physics, IA or any GPGPU task, understand well The differences between Vulkan for graphics and its use as a computing API It is key to choosing the right path.

Ultimately, Vulkan is just one, but it can be used as Traditional graphics API or general-purpose computing API Taking advantage of its compute shaders. Let's take a closer look at what changes compared to OpenGL, what the differences are with respect to CUDA, and how all of this fits in with the rest of the modern API ecosystem like DirectX or even OpenCL.

What is Vulkan and what does Vulkan Compute look like?

Vulkan is a low-level API Created and maintained by the Khronos Group as an evolution of the old OpenGL and the Mantle (AMD) philosophy. It was launched in 2016 as a modern API for 3D graphics and general GPU computing, with two very clear objectives: to get the most out of the hardware and be truly multiplatform.

Unlike OpenGL, which grew through extensions and patches, Vulkan was designed from scratch for multi-core CPUs, modern GPUs, and devices ranging from desktop PCs to mobile phones and consoles. It relies heavily on the idea that the developer assumes much of the resource management and synchronization, in exchange for a brutal reduction of the overload of the drivers.

When we talk about “Vulkan Compute”, we are actually referring to the use of compute shaders inside Vulkan For GPGPU tasks: simulations, image processing, particle physics, advanced post-processing, AI, etc. There isn't a separate API called that; what exists is a unified model where the graphics and computing shaders They share the same ecosystem: the same descriptor system, the same queue of commandssame memory and same synchronization tools.

This unified approach allows a single engine or application to manage rendering and computing with the same APIwithout needing to mix, for example, OpenGL for graphics and another separate compute API for general GPU work.

From OpenGL to Vulkan: the leap in compute shaders

OpenGL incorporated years ago the compute shadersThis made GPGPU reasonably comfortable for many tasks. But its design carries a global state machine, strong dependence on a single context and an overhead of validations, implicit locks and hidden synchronization that do not always scale well on multi-core CPUs.

In OpenGL, most of the API state is associated with a global context: The calls are made sequentially And the driver makes many "magical" decisions for you. For a small program or a demo, that might work, but when you want to truly parallelize, take advantage of all the CPU cores, and create well-optimized work queues, that approach falls short.

Vulkan breaks with this model and is based on objects without global stateInstead of calling functions that modify global states, in Vulkan you create command buffers where you describe exactly what you want to run: what resources to use, which shaders to launch, how to synchronize, etc. These commands can be generated in parallel from multiple threads and then sent to the GPU very efficiently.

This has a clear consequence: compared to OpenGL, Vulkan allows for much better utilization of multi-core CPUsIn Vulkan, compute shaders can be prepared and dispatched from multiple threads without the driver becoming a bottleneck. Therefore, in very demanding tasks or those with many small jobs, Vulkan is often used. outperform OpenGL simply by reducing CPU costs and allowing for better load distribution.

Another important difference lies in the shaders: OpenGL uses high-level GLSL Each driver must include its own compiler, which translates the source code into GPU binary at runtime. This complicates driver maintenance and can lead to differing behavior between manufacturers.

Vulkan adopts a similar approach to Direct3D: shaders are not delivered as text, but in a standard binary intermediate format called SPIR-VYou can compile your shaders once (for example, from GLSL or HLSL to the SPIR-V binary), and then the driver only needs to perform the final optimization. This improves los tiempos de cargoIt reduces runtime compilation errors and makes it easier to use. large number of different shaders in the same scene or computing pipeline.

Performance differences: OpenGL Compute vs Vulkan Compute

From a purely GPU-related point of view, the computational code that runs in a compute shader It can be very similar whether you write it for OpenGL or Vulkan. However, the overall performance of the application depends not only on what the GPU is running, but also on... How do you power that GPU from the CPU? and how you sync and share data.

With Vulkan you can eliminate much of the driver overheadUsing command queues explicitly, grouping many operations into batches, managing memory manually, and avoiding global states that block parallelism. All of this means that, in practice, The same simulation or the same general computation tends to perform better in Vulkan than in OpenGL, especially in scenarios with many threads or many small tasks.

In real-world applications, similar differences have also been seen in pure graphics: with identical hardware and in an environment such as Windows 7It has been measured that Vulkan can achieve around 303 fps compared to around 270 fps with another API more traditional in certain benchmarks, thanks precisely to this reduction in overhead and the better CPU/GPU load distribution.

Furthermore, Vulkan scales much better with multi-core CPUsOpenGL 4 and DirectX 11 were initially designed for single-core CPUs and have received extensions to work with multiple cores, but their model doesn't scale as well. Vulkan, on the other hand, was designed with that reality in mind from the ground up, so preparing work in parallel for the GPU is a natural part of the API, not an add-on.

Vulkan vs. DirectX: Modern Low-Level APIs

To place Vulkan Compute on the complete map, it is helpful to compare it with The complete comparison: Vulkan vs DirectX vs OpenGLespecially Direct3D 11 and 12, which remain a benchmark in the development of video games for Windows and Xbox.

DirectX, developed by Microsoft since 1995, is actually a set of multimedia APIs (graphics, audio, input, etc.). Direct3D is the 3D graphics component, and in Direct3D 11, the API is higher-level: much of the resource management, synchronization, and validation logic is handled automatically by the driver. This simplifies the developer's life but also implies more CPU overhead and less fine control.

With Direct3D 12, Microsoft moved closer to the Vulkan philosophy: Lower level API, explicit control and better scaling on multi-core CPUs. However, DirectX is a technology proprietary and focused on the Microsoft ecosystemWindows and Xbox consoles. If your primary focus is Windows, DirectX has a very mature ecosystem, extensive documentation, and a huge community. If you're unsure, you can to know which version of DirectX you have installed.

Vulkan, on the other hand, is multiplatform from minute one. Works on Linux, AndroidBSD Unix, Windows Nintendo Switch and other platforms, with third-party support on macOS and iOS through compatibility layers. This makes it especially attractive for studies that want to target multiple platforms with a single codebaseincluding PCs, mobiles and consoles.

Another point: DirectX is a API collection, while Vulkan is usually described as software programming next-generation video games/OpenGL but unified for graphics and computing. Vulkan offers a Very balanced CPU/GPU usage and it has positioned itself very well in Android and in the world of emulators, while DirectX clearly dominates in Windows.

In terms of raw speed, it is often said that Vulkan is faster than DirectX In many scenarios, especially when work is well parallelized and the multi-threaded, low-level nature of the API is leveraged, DirectX 12 can match or even surpass it in specific cases. To check compatibility, you can Check if your GPU is compatible with DirectX 12 Ultimate.

Vulkan vs CUDA for pure GPGPU

Another important player in general-purpose GPU computing is CUDA, the platform of NVIDIAIf you're coming from CUDA and are thinking about changing or complementing your stack, it's key to understand what you gain and lose with Vulkan Compute.

CUDA is designed specifically for scientific computing, HPC, AI, machine learning and intensive processing tasks. It offers a huge ecosystem of optimized libraries, debugging tools, and profiles, and is deeply integrated into the academic and supercomputing worlds. The C/C++ syntax with CUDA extensions is quite user-friendly if you're already familiar with that environment.

Vulkan, for its part, is a General-purpose graphics and computing APIIts compute shaders can do much of what you do in CUDA, but the mental model is different: you work with SPIR-V shadersDescriptor structures, command queues, and explicit synchronization are used. The syntax is not identical to CUDA; you will typically write in GLSL, HLSL, or another source language, which you will then compile to SPIR-V.

The great advantage of Vulkan over CUDA is that It is not limited to NVIDIA hardwareAs an open standard from Khronos, it works with GPUs from various manufacturers and on many different operating systems: Linux, Android, Windows, Nintendo, and more. If you need your GPGPU software to run on AMD, Intelmobile phones or consolesVulkan is a much more flexible solution.

However, the learning curve for Vulkan is usually... steeper than CUDA. A lot of infrastructure needs to be set up: logical devices, queues, buffers, descriptors, pipelines, manual synchronization, etc. In CUDA, many of these layers are more integrated or abstracted by NVIDIA's own libraries.

In terms of practical use, Vulkan is ideal when you need to unify graphics and computing and deploy on many platforms. CUDA is fantastic for pure GPGPU in the NVIDIA ecosystem, especially if you're going to rely on its library stack (cuBLAS, cuDNN, etc.) and don't need advanced graphics integrated into the same API.

Vulkan versus other APIs and standards: OpenCL, Metal, Mantle…

Vulkan doesn't stand alone against OpenGL, DirectX, and CUDA; there are other players in the parallel computing landscape. One important example is OpenCL, also from Khronos, which has been used for years for general-purpose parallel programming across multiple devices.

With the arrival of OpenCL 2.2, Khronos announced that OpenCL would converge with Vulkan Where possible, the OpenCL code should be deployable on both OpenCL and Vulkan. This approach has already been demonstrated in real-world products: for example, Adobe Premiere Rush uses the open-source compiler. clspv to convert OpenCL C kernels to SPIR-V and run them on a Vulkan runtime on Android.

There are also platform-specific APIs such as Metal (Apple) or the historical roots of Vulkan in MantleVulkan, AMD's API whose code was donated to Khronos to create a low-level, open standard similar to DirectX 12 but cross-platform, inherits many ideas from Mantle and shares a philosophy with DirectX 12 and Metal. Low overhead, explicit control, and focus on multi-core CPUs.

Evolution of Vulkan and focus on computing

Since its initial release in 2016, Vulkan has received several major updates which have been refining their capabilities for both graphics and compute shaders.

In Vulkan 1.1, extensions such as multiview, device groups, cross-process sharing, and APIsAdvanced computing functionality, improved HLSL compatibility, and support for color formats such as YCbCr were introduced. Explicit support was also added for multiple GPUs and docks for modern techniques such as ray tracing, getting even closer to the capabilities of DirectX 12.

Vulkan 1.2 integrated 23 additional extensions widely used in the base standard. Among the most important are the timeline semaphores for synchronization (much more manageable in complex scenarios), a formal memory model that precisely defines how operations are synchronized across different threads, and the indexing of descriptorswhich allows reusing descriptor designs with multiple shaders, something very useful when mixing graphics and computing.

Vulkan 1.3 continued in that vein, also integrating a good number of extensions and focusing on reduce fragmentationFor a device to be declared compatible with Vulkan 1.3, certain features cease to be optional and become mandatory, making it easier for developers to know exactly what set of capabilities they can expect. In addition, improvements were added to dynamic rendering, more dynamic states, and a refined synchronization APIall of this is very relevant when combining graphics with intensive computing.

In parallel, SPIR-V has also been progressing, going through versions such as 1.3, which allows express increasingly complex shaders and computing kernels and compatible with various high-level APIs.

Practical advantages of Vulkan for graphics and computing

Vulkan was designed to offer a number of clear advantages over previous APIs, both in graphics and general computing. One of the most obvious is the controller overload reductionThis translates to less work for the CPU. By using batch processing and command buffers, you can prepare large amounts of work for the GPU all at once.

Another advantage is the better scaling on multi-core CPUsDirectX 11 and OpenGL 4 were born in an era of single-core CPUs and were later patched to work better with multiple cores, but their models don't scale as well. Vulkan was designed specifically to allow multiple threads to prepare commands in parallel without overlapping, taking advantage of CPUs with many cores.

In terms of memory and synchronization, Vulkan gives you explicit control over GPU memory management and how the operations are synchronized. This prevents unexpected "magical" driver behavior, although it requires you to be much more careful. To help, instead of checking for runtime errors as OpenGL does, Vulkan separates validation into development layers which you can activate while debugging and deactivate in production to gain performance.

Another important advantage is that It unifies the management of compute cores and graphics shaders.There's no longer a need to use, for example, one API for graphics and another for compute; both rendering and GPGPU operate on the same model of queues, memory, and descriptors. This greatly simplifies projects where graphics and compute are highly intertwined, such as modern game engines, complex simulators, or applications of virtual reality and increased.