Open graphics processing chips

Open graphics processing chips are graphics processing unit (GPU) designs, architectures, or related computing systems that make important parts of their hardware design openly available for study, modification, implementation, or redistribution.

The concept is related to open hardware, open source, computer architecture, and graphics processing. An open graphics processing project may publish its hardware description language source code, instruction set architecture, documentation, software drivers, compilers, or other components needed to understand and use the processor.

There is not one universally accepted definition of an "open GPU." A system can be open at one layer while remaining closed at another. Learning about open graphics processing chips therefore involves examining which parts of a computing system are actually open and which parts are not.

What can be open?

A modern GPU is more than a physical chip.

It is part of a larger system involving hardware, firmware, drivers, compilers, programming interfaces, memory, and development tools.

Different projects may make different parts of this system open.

Instruction set architecture Hardware description language source code Processor microarchitecture Memory controller designs Graphics processing units Compute units Documentation

Device drivers Compilers Runtime software Firmware Development tools Simulation tools Reference hardware designs

An open driver for a proprietary GPU does not make the underlying GPU hardware open.

Likewise, an openly documented instruction set does not necessarily mean that the actual implementation of a commercial processor is open.

It is therefore useful to identify exactly what is being described when something is called an open GPU.

Hardware description languages

Digital chips can be designed using hardware description languages such as Verilog, SystemVerilog, and VHDL.

Instead of describing a program that runs on a processor, an HDL can describe the circuits and logic that become the processor itself.

An open GPU project can publish HDL source code that researchers and developers can inspect, simulate, modify, and potentially implement on an FPGA.

This makes open processor designs particularly useful for teaching computer architecture.

Students can examine how instructions are decoded, how parallel execution is scheduled, how caches operate, and how processing units communicate with memory.

A conventional commercial GPU generally cannot be studied internally at this level because much of its detailed hardware implementation is proprietary.

Open instruction sets

An instruction set architecture defines the instructions and programming model that software uses to communicate with a processor.

Open instruction sets can make experimentation with processor designs easier because developers do not necessarily need permission from a proprietary ISA owner to develop compatible implementations.

RISC-V is an important example of an open instruction set architecture used in processor research.

GPU research projects can extend or adapt open instruction sets for massively parallel computing, graphics, machine learning, and other workloads.

Open instruction sets do not automatically create open chips, but they can provide a foundation upon which open chip designs can be developed.

Examples of open GPU projects

Several projects demonstrate different approaches to open graphics processing hardware.

Vortex is an open-source RISC-V GPGPU project intended in part for GPU architecture research. It includes hardware designs along with compiler, driver, runtime, simulation, and FPGA support. Vortex can be used to study parallel computing and experiment with GPU architecture.

Libre-SOC is a project developing a libre hybrid processor architecture that combines CPU, vector processing, and GPU-related capabilities. Rather than treating the GPU as an entirely separate processor, the project explores integrating these capabilities into a common processor architecture.

MIAOW is an open-source implementation based on AMD's Southern Islands GPU instruction set architecture. It has been used primarily as a research platform rather than as a consumer graphics product.

These projects differ substantially in architecture, goals, maturity, and software support. They demonstrate that open graphics hardware can take several forms rather than following one design.

Graphics and general-purpose computing

Modern GPUs are useful for considerably more than displaying graphics.

Their large numbers of parallel processing units can perform calculations that would take substantially longer on processors designed primarily for sequential workloads.

This approach is commonly called general-purpose computing on graphics processing units or GPGPU computing.

GPU workloads can include:

• Computer graphics
• Scientific computing
• Physics simulations
• Machine learning
• Artificial intelligence
• Image processing
• Video processing
• Cryptography
• Engineering simulations
• Large numerical calculations

Open GPU hardware can therefore support research far beyond computer graphics.

Open GPUs and artificial intelligence

Modern artificial intelligence systems frequently depend on GPUs and other parallel accelerators.

Training and running large neural networks can require substantial amounts of computational power and memory bandwidth.

Much of the highest-performance accelerator market depends on proprietary processors, software ecosystems, and manufacturing supply chains.

Open graphics and accelerator architectures could provide additional opportunities for researchers to experiment with processors optimized for machine learning without being limited entirely to proprietary architectures.

However, designing a competitive GPU is extremely difficult.

High-performance processors depend on advanced semiconductor fabrication, memory systems, packaging, compilers, drivers, and extensive software optimization. Publishing an open chip design does not automatically make that chip inexpensive or easy to manufacture.

Manufacturing open chips

A chip design can be open even when the physical manufacturing process is not.

Modern semiconductor fabrication facilities cost billions of dollars and use highly specialized equipment.

An open processor project may provide files that describe the hardware while still relying on commercial semiconductor foundries to manufacture physical chips.

Projects can also be implemented on FPGAs, which allow digital hardware designs to be tested without manufacturing a custom integrated circuit.

This makes FPGAs particularly useful for education and experimental computer architecture.

A researcher can modify an open processor design, synthesize it for an FPGA, test it, collect performance information, and then modify the design again.

Benefits of open graphics hardware

Potential benefits include:

• Greater transparency into processor operation
• Improved opportunities for computer architecture education
• Independent security research
• Hardware experimentation
• Reduced dependence on proprietary architectures
• Development of specialized accelerators
• Easier academic research into GPU designs
• Long-term preservation of hardware knowledge
• Opportunities for collaborative hardware development

Open hardware can also make it easier to study whether claimed security or performance characteristics correspond with the actual design.

Challenges

Open GPUs face significant practical challenges.

Creating competitive graphics hardware requires expertise in chip architecture, parallel computing, memory systems, graphics APIs, compiler engineering, and semiconductor manufacturing.

Software compatibility is another major problem.

A powerful GPU has limited usefulness without reliable drivers, compilers, operating system integration, and applications capable of using it.

Commercial GPU companies have spent decades developing large software ecosystems.

An open GPU project therefore needs more than an interesting processor core. Successful hardware generally requires a usable software stack around it.

Learning and research activities

• Compare an open GPU architecture with a commercial GPU architecture.
• Download an open GPU hardware design and identify its major components.
• Simulate a simple GPU or vector processor.
• Research how GPUs schedule thousands of parallel threads.
• Compare CPUs, GPUs, and specialized AI accelerators.
• Study how an FPGA can be used to prototype a GPU.
• Research how open hardware licenses differ from software licenses.
• Examine the relationship between an ISA and a particular chip implementation.
• Compare open-source GPU drivers with open-source GPU hardware.
• Investigate the economic barriers to manufacturing an open GPU as a physical chip.

Discussion questions, essay ideas, and learning related AI prompt ideas

• What should qualify a graphics processor as open hardware?
• Is an open instruction set sufficient for calling a processor open?
• What advantages could fully open GPUs provide to universities and researchers?
• Why is developing an open GPU more difficult than developing many open-source software projects?
• Could an open GPU eventually compete with proprietary GPUs for artificial intelligence workloads?
• What parts of GPU development are most difficult to decentralize?
• How important are drivers and compilers compared with the processor hardware itself?
• Ask an AI system to explain the major components required to build a complete open GPU ecosystem.
• Ask an AI system to compare Vortex, Libre-SOC, and MIAOW as research platforms.
• Design a hypothetical open GPU intended primarily for educational use.
• Research how open GPU designs could contribute to technological problems involving access to computing resources.

Wikipedia readings

• Graphics processing unit
• General-purpose computing on graphics processing units
• Open-source hardware
• Hardware description language
• RISC-V
• Field-programmable gate array
• Instruction set architecture
• Graphics processing unit design
• OpenCL
• Vulkan

External readings

• Vortex GPGPU
• Libre-SOC
• MIAOW

See also

Graphics processing unit Open hardware Open source Computer architecture RISC-V Hardware description language FPGA Semiconductor Integrated circuit

Parallel computing Artificial intelligence Machine learning OpenCL Vulkan Computer graphics High-performance computing Technological problems Distributed computing