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We present TProf, an energy profiling tool for OpenMP-like task-parallel programs. To compute the energy consumed by each task in a parallel application, TProf dynamically traces the parallel execution and uses a novel technique to estimate the per-task energy consumption. To achieve this estimation, TProf apportions the total processor energy among cores and overcomes the limitation of current works which would otherwise make parallel accounting impossible to achieve. We demonstrate the value of TProf by characterizing a set of task parallel programs, where we find that data locality, memory access patterns and task working sets are responsible for significant variance in energy consumption between seemingly homogeneous tasks. In addition, we identify opportunities for fine-grain energy optimization by applying per-task Dynamic Voltage and Frequency Scaling (DVFS).

Abstract. The inherent difficulty of thread-based shared-memory programming has recently motivated research in high-level, task-parallel programming models. Recent advances of Task-Parallel models add implicit synchronization, where the system automatically detects and satisfies data dependencies among spawned tasks. However, dynamic dependence analysis incurs significant runtime overheads, because the runtime must track task resources and use this information to schedule tasks while avoiding conflicts and races. We present SCOOP, a compiler that effectively integrates static and dynamic analysis in code generation. SCOOP combines context-sensitive points-to, controlflow, escape, and effect analyses to remove redundant dependence checks at runtime. Our static analysis can work in combination with existing dynamic analyses and task-parallel runtimes that use annotations to specify tasks and their memory footprints. We use our static dependence analysis to detect non-conflicting tasks and an existing dynamic analysis to handle the remaining dependencies. We evaluate the resulting hybrid dependence analysis on a set of task-parallel programs.

Extending current Virtual Machine implementations to new Instruction Set Architectures entails a significant programming and debugging effort. Meta-circular VMs add another level of complexity towards this aim since they have to compile themselves with the same compiler that is being extended. Therefore, having low-level debugging tools is of vital importance in decreasing development time and bugs introduced. In this paper we describe our experiences in extending Maxine VM to the ARMv7 architecture. During that process, we developed a QEMU-based toolchain which enables us to debug a wide range of VM features in an automated way. The presented toolchain has been integrated with the JUNIT testing framework of Maxine VM and is capable of executing from simple assembly instructions to fully JIT compiled code. Furthermore, it is fully open-sourced and can be adapted to any other VMs seamlessly. Finally, we describe a compiler-assisted methodology that helps us identify, at runtime, faulty methods that generate no stack traces, in an automatic and fast manner. CCS Concepts • Software and its engineering → Justin-time compilers; Software testing and debugging;