- SHMT enables simultaneous and efficient utilization of CPU, GPU and accelerators IA, maximizing overall system performance.
- Tests have shown improvements of up to 1,95 times in processing speed and a power reduction of more than 50%.
- Adopting SHMT requires software adaptation and industry collaboration, but promises to revolutionize efficiency and sustainability.
La SHMT technology has burst onto the computing scene with the promise to revolutionize performance and energy efficiency of electronic devices, from personal computers to servers, including portable, smartphones and other connected devices. This advancement, supported by research from universities such as the University of California Riverside, not only represents a leap forward in the way we use our computing resources hardware, but opens the door to a new era of software optimization without the need to change physical components.
If you are a person passionate about technology, a professional in the IT sector or are simply curious about the future of processors and the use of Artificial IntelligenceHere you'll discover exactly what SHMT is, how it works, its tangible results, and the impact it can have on the world of hardware and software in the short and medium term.
What is SHMT technology? The concept that changes everything
SHMT are the initials in English of Simultaneous and Heterogeneous Multithreading, translated into Spanish as simultaneous and heterogeneous multithreadingThis technology consists of an algorithm and an approach to programming which allows joint and simultaneous use all processing units of a device, such as the CPU, the GPU and artificial intelligence accelerators (NPU, TPU and the like), treating the system as an integrated whole rather than as watertight compartments operating separately.
Traditionally, although our computers, mobile phones, or consoles may have several types of processors, each one typically handles its own tasks, and communication between them causes bottlenecks, inefficiencies, and wasted resources. SHMT challenges this classic model by ensuring that all of these cores and accelerators work together simultaneously to solve the same task, distributing the workload intelligently and dynamically depending on which is most appropriate at any given time.
The origin and scientific leap behind SHMT
The idea and first recognized implementation of SHMT comes from the team led by Professor Hung-Wei Tseng of the University of California RiversideIn their research, Tseng and his student Kuan-Chieh Hsu demonstrated under laboratory conditions that it is possible to coordinate different types of processing units using advanced software systems, achieving almost double the performance and reduce energy consumption by more than 50% in certain real-life tests.
The key to his proposal is the abstraction and simultaneous execution of heterogeneous threads, that is, getting the CPUs, GPUs, and AI accelerators to work simultaneously on a common code, instead of waiting for one to finish and sending the next one in turn.
How does SHMT work? A software model that leverages hardware
The operation of SHMT is based on a system of dynamic task assignment Using parallel programming algorithms, which constantly monitor the status and capabilities of each available processing unit. When a compatible application runs, the system distributes processes and threads among the CPU, GPU, and, if present, the NPU/TPU, deciding in real time which part is most efficient for each piece of code.
- Heterogeneous processes: The main novelty is to take advantage of the heterogeneity hardware. Code is no longer just executed in parallel on CPU cores or GPU threads, but can be used simultaneously, as well as using AI accelerators, combining resources and capabilities.
- No physical changes to the hardware: The most striking advantage of SHMT is that it doesn't require the introduction of new chips or the upgrade of physical components. It's a software-based evolution, which reduces costs and facilitates adoption in existing devices.
- Abstraction and runtime: Tseng's model includes an abstraction system and a runtime environment that simplifies the development of compatible applications. Programmers can delegate the optimal task distribution to the SHMT system, which does this automatically.
- Elimination of bottlenecks: By preventing a single unit, typically the slowest or most overloaded, from becoming the bottleneck, the system makes better use of all available resources. This results in shorter wait times, higher performance, and significant savings in energy consumption and heat generation.
Results and figures: How much does a system actually improve with SHMT?
The data obtained from laboratory tests have been conclusive. According to the University of California research and tests conducted by other teams, processing speed almost doubles, with specific figures of 1,95X faster delivery Compared to conventional architectures. The most surprising thing is that this performance increase is not accompanied by increased energy consumption, but quite the opposite:
- Reduction in energy consumption by 51%: In most experiments, the SHMT system managed to cut the energy needed to complete heavy tasks in half.
- Particularly noticeable improvements under intensive loads: The greatest advantages of SHMT emerge when the system is subject to high demands, such as in scientific applications, artificial intelligence, large simulations, or advanced gaming. In these situations, the bottlenecks of traditional architectures are much more evident, so the potential for improvement is greater.
- Four times more efficient than traditional multithreading: Overall efficiency improvements of up to four times have been observed compared to traditional multithreading schemes, where the CPU and GPU do not share the actual workload.
- Real example: NVIDIA Jetson Nano and ARM architectures: The SHMT implementation has been tested on platforms with 4-core ARM CPUs, dedicated GPUs, and Edge TPU AI accelerators. Google, achieving these spectacular results without any changes to the physical hardware.
This performance may vary depending on the task, but the figures show a significant increase in processing efficiency and speed.
Difficulties and challenges for the adoption of SHMT in the real world
Although the benefits of SHMT are promising, its widespread adoption is not without its obstacles. The biggest challenge lies in the need to adapt software and applications To be compatible with this model, developers must modify and optimize their programs to take advantage of intelligent task sharing, which requires additional effort compared to traditional development approaches.
- Gradual implementation: Not all systems and applications can benefit from SHMT immediately. It is necessary for OS, drivers and development tools evolve to natively support this model.
- Developers' work: Software adaptation is neither automatic nor trivial. It requires new scheduling strategies and extensive testing to ensure that the system assigns tasks to the most efficient unit at all times.
- Diverse impact depending on the type of application: The greatest benefits will be seen in programs that make intensive and simultaneous use of CPU, GPU, and AI accelerators, such as scientific simulations, rendering, artificial intelligence, high-end gaming, and server environments.
- Possible initial limitations: In some situations, or with poorly optimized code, the results may be less spectacular, or in very specific cases, the expected improvements may not be achieved.
Collateral benefits: sustainability and environmental footprint reduction
We're not just talking about performance and speed. SHMT brings environmental and economic benefitsBy reducing the need to increase hardware power or install new chips, costs are reduced and the generation of electronic waste is reduced. Lower energy consumption means lower carbon emissions and, in the case of large data centers, even less water use for cooling, helping to conserve natural resources.
Professor Tseng himself emphasized this aspect in his statements: “There’s no need to add new processors because you already have them.”The key is to make better use of what already exists, which is especially relevant for infrastructure such as servers, supercomputers, and embedded systems in everyday devices.
For which devices and platforms is SHMT relevant?
SHMT technology is compatible with any platform that combines different types of processing units. Laboratory tests have been conducted on:
- Personal computers (PCs) and laptops with dedicated CPU and GPU.
- Mobile devices such as smartphones and tablets with multicore processors and integrated AI units.
- Embedded Systems and platforms IoT with Arm chips, Google Edge TPU accelerators, and NVIDIA GPUs.
- Servers and data centers that have high-performance CPUs, multiple GPUs and FPGAs or TPUs designed for artificial intelligence and big data processing.
- Handheld PCs and portable consoles , the Steam Deck or Asus ROG Ally, which seek to maximize efficiency and performance in a limited space.
To take full advantage of SHMT, it is essential that the operating system and applications are prepared, an evolution that will come progressively as manufacturers and developers adopt these new parallel programming techniques.
Impact on the industry and future prospects
The development and implementation of SHMT is still in its early stages, but the potential is enormous. Industry articles and experts agree that, as development tools become more established and operating systems natively support this technology, we will see:
- Major changes in software design and programming, prioritizing optimization for heterogeneous environments and improving the user experience.
- Higher energy efficiency with the same infrastructure, minimizing costs and extending the useful life of electronic devices.
- Reducing the environmental footprint, by avoiding constant hardware upgrades and cutting energy consumption and water use for cooling.
- Boosting artificial intelligence and parallel processing, allowing AI applications to benefit from the full power of available hardware.
The main challenge will be for developers, chip manufacturers, and software companies to work together to standardize SHMT and provide full support at the operating system and application levels.
References and academic support from SHMT
The SHMT model is based on recent scientific publications, such as the one presented by Tseng and Hsu in the IEEE/ACM International Symposium On Microarchitecture, as well as expert analysis published in journals such as the Archives of Computational Methods in Engineering. The proofs of concept have been peer-reviewed, and the results have been replicated in various environments, lending strength to claims about SHMT's potential for heterogeneous computing.
The technology is still under investigation to improve its integration, optimization, and facilitate programming, but the first steps have already been scientifically validated, positioning SHMT as one of the most promising approaches to high-performance and efficient computing in the near future.
It's important to note that the advancement represented by SHMT demonstrates that much of the potential of our current devices is being wasted through traditional processing methods, and its progressive adoption will enable significant improvements without the need to acquire new hardware. Industry will be able to reduce costs and reduce its environmental footprint, while developers will have new tools to create faster, more efficient, and more sustainable applications.
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