Graphics Processing Units (GPU) are increasingly deployed on Cyber-physical Systems (CPSs), frequently used to perform real-time safety-critical functions, such as object detection on autonomous vehicles. As a result, availability is important for GPU tasks in CPS platforms. However, existing Trusted Execution Environments (TEE) solutions with availability guarantees focus only on CPU computing.To bridge this gap, we propose AvaGPU, a TEE that guarantees real-time availability for CPU tasks involving GPU execution under compromised OS. There are three technical challenges. First, to prevent malicious resource contention due to separate scheduling of CPU and GPU tasks, we proposed a CPU-GPU co-scheduling framework that couples the priority of CPU and GPU tasks. Second, we propose software-based secure preemption on GPU tasks to bound the degree of priority inversion on GPU. Third, we propose a new split design of GPU driver with minimized Trusted Computing Base (TCB) to achieve secure and efficient GPU management for CPS. We implement a prototype of AvaGPU on the Jetson AGX Orin platform. The system is evaluated on benchmark, synthetic tasks, and real-world applications with 15.87% runtime overhead on average.
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Exploration on Task Scheduling Strategy of CPU-GPU Heterogeneous Computing System
For a CPU-GPU heterogeneous computing system,
different types of processors have load balancing problems in
the calculation process. What’s more, multitasking cannot be
matched to the appropriate processor core is also an urgent
problem to be solved. In this paper, we propose a task
scheduling strategy for high-performance CPU-GPU
heterogeneous computing platform to solve these problems. For
the single task model, a task scheduling strategy based on loadaware
for CPU-GPU heterogeneous computing platform is
proposed. This strategy detects the computing power of the CPU
and GPU to process specified tasks, and allocates computing
tasks to the CPU and GPU according to the perception ratio.
The tasks are stored in a bidirectional queue to reduce the
additional overhead brought by scheduling. For the multi-task
model, a task scheduling strategy based on the genetic algorithm
for CPU-GPU heterogeneous computing platform is proposed.
The strategy aims at improving the overall operating efficiency
of the system, and accurately binds the execution relationship
between different types of tasks and heterogeneous processing
cores. Our experimental results show that the scheduling
strategy can improve the efficiency of parallel computing as well
as system performance.
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- Award ID(s):
- 1828105
- NSF-PAR ID:
- 10347479
- Date Published:
- Journal Name:
- 2020 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
