Abstract. Heterogeneous architectures are currently widespread. With the advent of easy-to-program general purpose GPUs, virtually every recent desktop computer is a heterogeneous system. Combining the CPU and the GPU brings great amounts of processing power. However, such architectures are often used in a restricted way for domain-specific applications like scientific applications and games, and they tend to be used by a single application at a time. We envision future heterogeneous computing systems where all their heterogeneous resources are continuously utilized by different applications with versioned critical parts to be able to better adapt their behavior and improve execution time, power consumption, response time and other constraints at runtime. Under such a model, adaptive scheduling becomes a critical component. In this paper, we propose a novel predictive user-level scheduler based on past performance history for heterogeneous systems. We developed several scheduling policies and present the study of their impact on system performance. We demonstrate that such scheduler allows multiple applications to fully utilize all available processing resources in CPU/GPUlike systems and consistently achieve speedups ranging from 30% to 40% compared to just using the GPU in a single application mode.

No abstract

No abstract

Library OSs have been proposed to deploy applications isolated inside containers, VMs, or trusted execution environments. They often follow a highly modular design in which third-party components are combined to offer the OS functionality needed by an application, and they are customised at compilation and deployment time to fit application requirements. Yet their monolithic design lacks isolation across components: when applications and OS components contain security-sensitive data (e.g., cryptographic keys or user data), the lack of isolation renders library OSs open to security breaches via malicious or vulnerable third-party components.We describe CubicleOS, a library OS that isolates components in the system while maintaining the simple, monolithic development approach of library composition. CubicleOS allows isolated components, called cubicles, to share data dynamically with other components. It provides spatial memory isolation at the granularity of function calls by using Intel MPK at user-level to isolate components. At the same time, it supports zero-copy data access across cubicles with feature-rich OS functionality. Our evaluation shows that CubicleOS introduces moderate end-to-end performance overheads in complex applications: 2× for the I/O-intensive NGINX web server with 8 partitions, and 1.7-8× for the SQLite database engine with 7 partitions. CCS CONCEPTS• Software and its engineering → Message passing; • Security and privacy → Operating systems security; Software and application security.