Hardware-assisted garbage collection
Hardware-assisted garbage collection is the use of specialized hardware mechanisms to improve the efficiency and performance of garbage collection in computer systems. This approach integrates hardware support directly into the processor or memory system to handle tasks traditionally managed by software, such as object allocation, reference counting, or mark-and-sweep operations. It is particularly relevant in real-time systems, embedded systems, and high-performance computing environments where software-only garbage collection may introduce unacceptable pauses or overhead.
History
[edit]Research into hardware-assisted garbage collection dates back to the 1990s. Early work focused on simulation studies to analyze the behavior of such systems.[1] By the early 2000s, proposals emerged for integrating garbage collection into hardware for real-time embedded systems.[2] Modern developments include accelerators for tracing garbage collection and concurrent collectors for functional languages.[3]
The concept of hardware-assisted garbage collection dates back several decades, with early implementations in specialized Lisp machines and research prototypes. Notable historical systems like The Garbage Collected Memory Module, a near-memory co-processor design[4] or Azul Systems' Vega processors, which included hardware support for read barriers to enable pauseless garbage collection[5]
While many early proposals did not achieve widespread adoption, renewed interest has emerged due to the slowing of Moore's law, the prevalence of garbage-collected languages, and the rise of cloud computing and hardware accelerators.
Mechanisms
[edit]Hardware assistance can involve dedicated instructions for memory allocation, reference tracking, or collection phases. For example, some architectures provide support for bitmap-marking or concurrent marking to reduce pauses.[6] In cloud environments, hardware instructions accelerate garbage collection hotspots, improving performance by an order of magnitude.[7][8]
Generational schemes, as noted in general garbage collection literature, can be enhanced with hardware support for real-time operations. Proposals include integrated hardware collectors that run continuously in the background for embedded systems.[9]
Applications
[edit]This technology is explored in contexts like virtual machines for cloud computing, where middleware overhead is reduced. It is also relevant for non-strict functional languages with concurrent collectors.[10] Hardware accelerators have been proposed for tracing garbage collection in modern architectures.[11]
Implementations in the wild
[edit]| System / CPU | Year introduced | Hardware feature(s) | Collector type |
|---|---|---|---|
| Symbolics 3600 | 1983 | Per-word tags, list micro-ops | Incremental, generational |
| IBM Wortmann GC FPGA | 2014 | RTL heap walker | Stall-free realtime |
| Azul Vega 3 | 2010 | Barrier opcode, concurrent remap unit | C4 pauseless generational |
| Armv9-A (MTE) | 2021 | 4-bit memory/pointer tags | Concurrent, safety-aided |
Performance
[edit]In controlled benchmarks, off-loading the mark phase to a hardware accelerator cut GC time by 65–80% and total application time by up to 25%.[12] Data-centre JVM studies reported consistent sub-1 ms tail-latency for 99.999 th percentile pauses on 256 GB heaps using Azul's C4 on Vega hardware.[13]
Advantages and challenges
[edit]Hardware assistance offers reliable operation, higher performance, and minimal pauses, making it suitable for real-time systems.[14] However, it may require custom hardware, limiting adoption in general-purpose CPUs.[15]
See also
[edit]- Garbage collection (computer science)
- Memory management
- Real-time computing
- Tracing garbage collection
- Reference counting
- Lisp machine
References
[edit]- ^ "Progress in hardware-assisted real-time garbage collection". Memory Management. SpringerLink. 18 December 1995. Retrieved 27 October 2025.
- ^ "Hardware-Assisted General-Purpose Garbage Collection for Hard Real-Time Systems". Iowa State University. 31 October 1992. Retrieved 27 October 2025.
- ^ "Integrated Hardware Garbage Collection". ACM Transactions on Embedded Computing Systems. 9 July 2021. Retrieved 27 October 2025.
- ^ Schmidt, William J.; Nilsen, Kelvin D. (1994). "Performance of a hardware-assisted real-time garbage collector". ACM SIGPLAN Notices. 29 (11): 76–85. Retrieved 27 October 2025.
- ^ Maas, Martin; Asanović, Krste; Kubiatowicz, John. "A New Proposal for Hardware-assisted Garbage Collection" (PDF). University of California, Berkeley. Retrieved 27 October 2025.
- ^ "functional programming - Hardware Assisted Garbage Collection - Stack Overflow". Stack Overflow. 12 November 2011. Retrieved 27 October 2025.
- ^ "Achieving middleware execution efficiency: hardware-assisted garbage collection operations". The Journal of Supercomputing. 9 November 2010. Retrieved 27 October 2025.
- ^ "Hardware-assisted middleware: Acceleration of garbage collection operations". IEEE Conference Publication. IEEE Xplore. Retrieved 27 October 2025.
- ^ "Integrated hardware garbage collection for real-time embedded systems - University of Bristol". University of Bristol. 28 September 2021. Retrieved 27 October 2025.
- ^ "Cloaca: A Concurrent Hardware Garbage Collector for Non-strict Functional Languages". Proceedings of the 17th ACM SIGPLAN International Haskell Symposium. Retrieved 27 October 2025.
- ^ Maas, Martin; Asanovic, Krste; Kubiatowicz, John. "A hardware accelerator for tracing garbage collection". A Hardware Accelerator for Tracing Garbage Collection. Retrieved 27 October 2025.
- ^ Maas, Philipp (June 2018). "A Hardware Accelerator for Tracing Garbage Collection". Proceedings of the 45th International Symposium on Computer Architecture: 98–110. Retrieved 27 October 2025.
- ^ Azul Systems (2025). "Azul C4 Garbage Collector". Azul.com. Azul Systems. Retrieved 27 October 2025.
- ^ "Richard Jones' Garbage Collection Bibliography". 15 January 2025. Retrieved 27 October 2025.
- ^ "blogorrhea: Hardware-assisted garbage collection". Retrieved 27 October 2025.