arXiv:2607.02630v1 Announce Type: cross Abstract: Hardware accelerators now sit on the critical path of online serving. GPUs, FPGAs, and increasingly remote services such as hardware security modules, post-quantum KEMs, and inference servers. For fine-grained offloads (microseconds to a few milliseconds) the classic responses to the resulting stall both fail: a context switch costs as much as the offload, and a busy-wait burns the core. Overlapping the offload with other requests is the fix, and prior systems obtain it by adding concurrency: an async-framework rewrite, a new runtime or datapla
The increasing reliance on hardware accelerators for online serving, coupled with their fine-grained performance requirements, makes efficient computation offloading a critical challenge for current server architectures.
Efficiently integrating specialized hardware like GPUs, FPGAs, and remote services with general-purpose servers is crucial for scaling AI and other compute-intensive applications, impacting operational costs and performance ceilings.
This research suggests a more streamlined approach to computation offload, potentially moving away from complex framework rewrites and towards simpler, more direct implementations for leveraging accelerators.
- · Cloud providers
- · Accelerator hardware manufacturers
- · AI/ML service providers
- · High-performance computing sectors
- · Traditional CPU-centric compute providers
- · Developers reliant on complex async frameworks
- · Legacy data center infrastructure
Improved performance and reduced latency for online services heavily reliant on hardware accelerators.
Lower operational costs for data centers due to more efficient use of both CPU and specialized hardware resources.
Accelerated development and wider adoption of specialized hardware for various compute tasks, further decentralizing computational power.
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Read at arXiv cs.AI