GSLICE: controlled spatial sharing of GPUs for a scalable inference platform

The increasing demand for cloud-based inference services requires the use of Graphics Processing Unit (GPU). It is highly desirable to utilize GPU efficiently by multiplexing different inference tasks on the GPU. Batched processing, CUDA streams and Multi-process-service (MPS) help. However, we find that these are not adequate for achieving scalability by efficiently utilizing GPUs, and do not guarantee predictable performance. GSLICE addresses these challenges by incorporating a dynamic GPU resource allocation and management framework to maximize performance and resource utilization. We virtualize the GPU by apportioning the GPU resources across different Inference Functions (IFs), thus providing isolation and guaranteeing performance. We develop self-learning and adaptive GPU resource allocation and batching schemes that account for network traffic characteristics, while also keeping inference latencies below service level objectives. GSLICE adapts quickly to the streaming data's workload intensity and the variability of GPU processing costs. GSLICE provides scalability of the GPU for IF processing through efficient and controlled spatial multiplexing, coupled with a GPU resource re-allocation scheme with near-zero (< 100μs) downtime. Compared to default MPS and TensorRT, GSLICE improves GPU utilization efficiency by 60--800% and achieves 2--13X improvement in aggregate throughput.