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In today's computing world, heterogeneous computing is gradually becoming the mainstream.This technology utilizes a number of different types of processors or cores, designed to improve performance and energy efficiency.As system design evolves, past challenges and opportunities still depend on how we deal with these new computing architectures.
The core concept in heterogeneous computing systems is to achieve performance improvements by not just adding the same type of processor, but adding heterogeneous coprocessors with specialized processing capabilities.
Background of heterogeneous calculation
Heterogeneous computing systems usually involve different instruction set architectures (ISAs), and this heterogeneity is not only reflected in the differences in microarchitectures, but also in the reconstruction of design and computing models.Modern heterogeneous system architecture (HSA) eliminates the cumbersomeness of users when using multiprocessor types, allowing the CPU and GPU to operate efficiently on the same chip.This allows general GPUs to perform mathematical calculations and enables the CPU to take charge of operating systems and traditional serial tasks.
Latest research shows that leveraging the diversity brought by multiple ISAs, heterogeneous ISA chip multiprocessors have up to 21% performance and save 23% energy than the optimal homogeneous architecture of the same ISA. Reduce energy delay products (EDP) by 32%.
Heterogeneous CPU Topology
In heterogeneous CPU topology, although the same ISA is used, the speed and performance differences between cores can significantly affect the performance of the overall system.Here, the core is usually divided into high-performance cores (usually called "big cores") and more energy-efficient cores (called "small cores").This topology is often used to provide better energy efficiency, especially for applications on mobile systems.
Challenges of heterogeneous computing
Although heterogeneous computing systems provide potential performance gains, they also present many challenges that are not common in traditional homogeneous systems.Here are some of the main challenges:
- Different instruction set architectures cause binary incompatibility.
- Different application binary interfaces (ABIs) may interpret memory in different ways.
- Open application programming interface (API) may not be available for all computing components.
- Differences in memory interface and levels may lead to heterogeneity of memory access.
- Different processors require different development tools, which increases the complexity of software development.
- The partitioning of data becomes a major challenge, and when it comes to complex computing systems, this problem even proves to be a complete problem of NP.
Practical Cases and Applications
Heterogeneous computing hardware spans a wide range of computing fields, from high-end servers to low-power embedded devices, including smartphones and tablets.For example, many new processors now have built-in logic to interface with other devices, including SATA, PCI, Ethernet, USB, etc.In addition, there are a large number of hardware accelerators, such as GPUs and encryption coprocessors, which make the application of heterogeneous systems more extensive.
For example, ARM's big.LITTLE technology combines high-performance high-power cores with low-power slow cores to achieve the effect of improving energy efficiency.
Looking forward
As technology continues to advance, the potential of heterogeneous computing systems looks to continue to expand.But if we want to solve the challenges in heterogeneous computing, we must think about how to more effectively integrate different computing components, improve program design models, and improve programming transparency.In the future, how should we face the opportunities and challenges brought by heterogeneous computing so that technology can better serve human beings?
