Syed W. Ali Website

Asymptotically Optimal Multiprocessor Real-Time Locking for non-JLFP Scheduling

Authors: Zelin Tong, Syed W. Ali, James H. Anderson

Proceedings of the 31st IEEE Real-Time and Embedded Technology and Applications Symposium, May 2025

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Abstract: In prior work, a number of asymptotically optimal suspension-based real-time locking protocols have been presented for job-level fixed priority (JLFP) schedulers, where job priorities do not change. However, the optimality proofs for these locking protocols break down under non-JLFP scheduling, where job priorities can vary. In fact, the problem of designing an asymptotically optimal real-time locking protocol for general non-JLFP scheduling has remained open. This paper closes this problem by presenting the non-JLFP locking protocol (NJLP), the first asymptotically optimal suspension-based real-time locking protocol for non-JLFP schedulers.

On the Necessity of Real-Time Principles in GPU-Driven Autonomous Robots

Authors: Syed W. Ali, Angelos Angelopoulos, Denver Massey, Sarah Haddix, Alexander Georgiev, Joseph Goh, Rohan Wagle, Prakash Sarathy, James H. Anderson, Ron Alterovitz

Proceedings of the 2025 IEEE International Conference on Robotics and Automation (ICRA), March 2025

Abstract: Robot autonomy is driving an ever-increasing demand for computational power, including on-board multi-core CPUs and accelerators such as GPUs, to enable fast perception, planning, control, and more. Careful scheduling of these computational tasks on the CPU cores and GPUs is important to prevent locking up the finite computational capacity in ways that hinder other critical workloads; delays in computing time-critical tasks like obstacle detection and control can have huge negative consequences for autonomous robots, potentially resulting in damage, substantial financial loss, or even loss of life. In this paper, we leverage recent advances from real-time systems research. We apply TimeWall, a component-based real-time framework, to the computational components of an autonomous drone and experimentally show that the timeliness and safe operation properties of a drone are preserved even in the presence of increasing interfering computational processes.

Concurrent FFT Execution on GPUs in Real-Time

Authors: Syed W. Ali, Joseph Goh, Samarjit Chakraborty, James H. Anderson

Proceedings of the 33rd Euromicro International Conference on Parallel, Distributed and Network-Based Processing, March 2025

Abstract: Fourier transforms are vital for a broad range of signal-processing applications. Accelerating FFTs with GPUs offers an orders-of-magnitude improvement vs. CPU-only FFT computation. However, two problems arise when executing FFT tasks with other GPU work. First, concurrent GPU use introduces unpredictability in the form of lengthy response times. Second, it is unclear how to best parameterize and schedule FFT tasks to meet the throughput and timeliness constraints of real-time signal processing. This work investigates how FFT and other GPU-using tasks can concurrently access a GPU while maintaining bounded response-time guarantees without sacrificing throughput. In our experiments, the techniques proposed by this work result in an up to 17% improvement in worst-case FFT response times.

Predictable GPU Sharing in Component-Based Real-Time Systems

Authors: Syed W. Ali, Zelin Tong, Joseph Goh, James H. Anderson

In Proceedings of the 36th Euromicro Conference on Real-Time Systems (ECRTS) July, 2024

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Abstract: This paper presents a real-time locking protocol whose design was motivated by the goal of enabling safe GPU sharing in time-sliced component-based systems. This locking protocol enables a GPU to be shared concurrently across, and utilized within, isolated components with predictable execution times. It relies on a novel resizing technique where GPU work is dimensioned on-the-fly to run on partitions of an NVIDIA GPU. This technique can be applied to any component that internally utilizes global CPU scheduling. The proposed locking protocol enables increased GPU parallelism and reduces GPU capacity loss with analytically provable benefits.