Multi-Language Edit-and-Continue for the Masses

We present an Edit-and-Continue implementation that allows regular source files to be treated like interactively updatable, compiled scripts, coupling the speed of compiled native machine code, with the ability to make changes without restarting. Our implementa-tion is based on the Microsoft .NET Framework and allows applications written in any .NET language to be dynamically updatable. Our solution works with the standard version of the Microsoft Common Language Runtime, and does not require a custom compiler or runtime. Because no application changes are needed, it is transparent to the application developer. The runtime overhead of our implementation is low enough to support updating real-time applications (e.g., interactive 3D graphics applications)

Multi-Language Edit-and-Continue for the Masses

We present an Edit-and-Continue implementation that allows regular source files to be treated like interactively updatable, compiled scripts, coupling the speed of compiled native machine code, with the ability to make changes without restarting. Our implementa-tion is based on the Microsoft .NET Framework and allows applications written in any .NET language to be dynamically updatable. Our solution works with the standard version of the Microsoft Common Language Runtime, and does not require a custom compiler or runtime. Because no application changes are needed, it is transparent to the application developer. The runtime overhead of our implementation is low enough to support updating real-time applications (e.g., interactive 3D graphics applications)

Pro++: A Profiling Framework for Primitive-based GPU Programming

Parallelizing software applications through the use of existing optimized primitives is a common trend that mediates the complexity of manual parallelization and the use of less efficient directive-based programming models. Parallel primitive libraries allow software engineers to map any sequential code to a target many-core architecture by identifying the most computational intensive code sections and mapping them into one ore more existing primitives. On the other hand, the spreading of such a primitive-based programming model and the different GPU architectures have led to a large and increasing number of third-party libraries, which often provide different implementations of the same primitive, each one optimized for a specific architecture. From the developer point of view, this moves the actual problem of parallelizing the software application to selecting, among the several implementations, the most efficient primitives for the target platform. This paper presents Pro++, a profiling framework for GPU primitives that allows measuring the implementation quality of a given primitive by considering the target architecture characteristics. The framework collects the information provided by a standard GPU profiler and combines them into optimization criteria. The criteria evaluations are weighed to distinguish the impact of each optimization on the overall quality of the primitive implementation. The paper shows how the tuning of the different weights has been conducted through the analysis of five of the most widespread existing primitive libraries and how the framework has been eventually applied to improve the implementation performance of two standard and widespread primitives

An Enhanced Profiling Framework for the Analysis and Development of Parallel Primitives for GPUs

Parallelizing software applications through the use of existing optimized primitives is a common trend that mediates the complexity of manual parallelization and the use of less efficient directive-based programming models. Parallel primitive libraries allow software engineers to map any sequential code to a target many-core architecture by identifying the most computational intensive code sections and mapping them into one ore more existing primitives. On the other hand, the spreading of such a primitive-based programming model and the different GPU architectures have led to a large and increasing number of thirdparty libraries, which often provide different implementations of the same primitive, each one optimized for a specific architecture. From the developer point of view, this moves the actual problem of parallelizing the software application to selecting, among the several implementations, the most efficient primitives for the target platform. This paper presents a profiling framework for GPU primitives, which allows measuring the implementation quality of a given primitive by considering the target architecture characteristics. The framework collects the information provided by a standard GPU profiler and combines them into optimization criteria. The criteria evaluations are weighed to distinguish the impact of each optimization on the overall quality of the primitive implementation. The paper shows how the tuning of the different weights has been conducted through the analysis of five of the most widespread existing primitive libraries and how the framework has been eventually applied to improve the implementation performance of a standard primitive