Tobias Pfandzelter
ABSTRACT
With the slowing of Moore's law and decline of Dennard scaling, computing systems increasingly rely on specialized hardware accelerators in addition to general-purpose compute units. Increased hardware heterogeneity necessitates disaggregating applications into workflows of fine-grained tasks that run on a diverse set of CPUs and accelerators. Current accelerator delivery models cannot support such applications efficiently, as (1) the overhead of managing accelerators erases performance benefits for fine-grained tasks; (2) exclusive accelerator use per task leads to underutilization; and (3) specialization increases complexity for developers.
We propose adopting concepts from Function-as-a-Service (FaaS), which has solved these challenges for general-purpose CPUs in cloud computing. Kernel-as-a-Service (KaaS) is a novel serverless programming model for generic compute accelerators that aids heterogeneous workflows by combining the ease-of-use of higher-level abstractions with the performance of low-level hand-tuned code. We evaluate KaaS with a focus on the breadth of the idea and its generality to diverse architectures rather than on an in-depth implementation for a single accelerator. Using proof-of-concept prototypes, we show that this programming model provides performance, performance efficiency, and ease-of-use benefits across a diverse range of compute accelerators. Despite increased levels of abstraction, when compared to a naive accelerator implementation, KaaS reduces completion times for fine-grained tasks by up to 96.0% (GPU), 68.4% (FPGA), 98.6% (TPU), and 34.9% (QPU) in our experiments.
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Index Terms
- Kernel-as-a-Service: A Serverless Programming Model for Heterogeneous Hardware Accelerators
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