Dynamically diagnosing type errors in unsafe code

Existing approaches for detecting type errors in unsafe languages are limited. Static analysis methods are imprecise, and often require source-level changes, while most dynamic methods check only memory properties (bounds, liveness, etc.), owing to a lack of run-time type information. This paper describes libcrunch, a system for binary-compatible run-time type checking of unmodified unsafe code, currently focusing on C. Practical experience shows that our prototype implementation is easily applicable to many real codebases without source-level modification, correctly flags programmer errors with a very low rate of false positives, offers a very low run-time overhead, and covers classes of error caught by no previously existing tool

Methodology for Refinement and Optimization of Dynamic Memory Management for Embedded Systems in Multimedia Applications

In multimedia applications, run-time memory management support has to allow real-time memory de/allocation, retrieving and processing of data. Thus, its implementation must be designed to combine high speed, low power, large data storage capacity and a high memory bandwidth. In this paper, we assess the performance of our new system-level exploration methodology to optimise the memory management of typical multimedia applications in an extensively used 3D reconstruction image system. This methodology is based on an analysis of the number of memory accesses, normalised memory footprint1 and energy estimations for the system studied. This results in an improvement of normalised memory footprint up to 44.2% and the estimated energy dissipation up to 22.6% over conventional static memory implementations in an optimised version of the driver application. Finally, our final version is able to scale perfectly the memory consumed in the system for a wide range of input parameters whereas the statically optimised version is unable to do this

Real-time high-performance computing for embedded control systems

The real-time control systems industry is moving towards the consolidation of multiple computing systems into fewer and more powerful ones, aiming for a reduction in size, weight, and power. The increasing demand for higher performance in other critical domains like autonomous driving has led the industry to recently include embedded GPUs for the implementation of advanced functionalities. The highly parallel architecture of GPUs could also be leveraged in the control systems industry to develop more advanced, energy-efficient, and scalable control systems. However, the closed-source and non-deterministic nature of GPUs complicates the resource provisioning analysis required for the implementation of critical real-time systems. On the other hand, there is no indication of the integration of GPUs in the traditional development cycle of control systems, which is oriented to the use of a model-based design approach. Recently, some model-based design tools vendors have extended their development frameworks with GPU code generation capabilities targeting hybrid computing platforms, so that the model-based design environment now enables the concurrent analysis of more complex and diverse functions by simulation and automating the deployment to the final target. However, there is no indication whether these tools are well-suited for the design and development of time-sensitive systems. Motivated by these challenges, in this thesis, we contribute to the state of the art of real-time control systems towards the adoption of embedded GPUs by providing tools to facilitate the resource provisioning analysis and the integration in the model-based design development cycle. First, we present a methodology and an automated tool to extract the properties of GPU memory allocators. This tool allows the computation of the real amount of memory used by GPU applications, facilitating a correct resource provisioning analysis. Then, we present a library which allows the characterization of the use of dynamic memory in GPU applications. We use this library to characterize GPU benchmarks and we identify memory allocation patterns that could be modified to improve performance and memory consumption when targeting embedded GPUs. Based on these results, we present a tool to optimize the use of dynamic memory in legacy GPU applications executed on embedded platforms. This tool allows us to minimize the memory consumption and memory management overhead of GPU applications without rewriting them. Afterwards, we analyze the timing of control algorithms executed in embedded GPUs and we identify techniques to achieve an acceptable real-time behavior. Finally, we evaluate model-based design tools in terms of integration with GPU hardware and GPU code generation, and we propose improvements for the model-based generated GPU code. Then, we present a source-to-source transformation tool to automatically apply the proposed improvements.La industria de los sistemas de control en tiempo real avanza hacia la consolidación de múltiples sistemas informáticos en menos y más potentes sistemas, con el objetivo de reducir el tamaño, el peso y el consumo. La creciente demanda de un mayor rendimiento en otros dominios críticos, como la conducción autónoma, ha llevado a la industria a incluir recientemente GPU embebidas para la implementación de funcionalidades avanzadas. La arquitectura altamente paralela de las GPU también podría aprovecharse en la industria de los sistemas de control para desarrollar sistemas de control más avanzados, eficientes energéticamente y escalables. Sin embargo, la naturaleza privativa y no determinista de las GPUs complica el análisis de aprovisionamiento de recursos requerido para la implementación de sistemas críticos en tiempo real. Por otro lado, no hay indicios de la integración de las GPU en el ciclo de desarrollo tradicional de los sistemas de control, que está orientado al uso de un enfoque de diseño basado en modelos. Recientemente, algunos proveedores de herramientas de diseño basado en modelos han ampliado sus entornos de desarrollo con capacidades de generación de código de GPU dirigidas a plataformas informáticas híbridas, de modo que el entorno de diseño basado en modelos ahora permite el análisis simultáneo de funciones más complejas y diversas mediante la simulación y la automatización de la implementación para el objetivo final. Sin embargo, no hay indicación de si estas herramientas son adecuadas para el diseño y desarrollo de sistemas sensibles al tiempo. Motivados por estos desafíos, en esta tesis contribuimos al estado del arte de los sistemas de control en tiempo real hacia la adopción de GPUs integradas al proporcionar herramientas para facilitar el análisis de aprovisionamiento de recursos y la integración en el ciclo de desarrollo de diseño basado en modelos. Primero, presentamos una metodología y una herramienta automatizada para extraer las propiedades de los asignadores de memoria en GPUs. Esta herramienta permite el cómputo de la cantidad real de memoria utilizada por las aplicaciones GPU, facilitando un correcto análisis del aprovisionamiento de recursos. Luego, presentamos una librería que permite la caracterización del uso de memoria dinámica en aplicaciones de GPU. Usamos esta librería para caracterizar una serie de benchmarks GPU e identificamos patrones de asignación de memoria que podrían modificarse para mejorar el rendimiento y el consumo de memoria al utilizar GPUs embebidas. Con base en estos resultados, presentamos también una herramienta para optimizar el uso de la memoria dinámica en aplicaciones de GPU heredadas al ser ejecutadas en plataformas embebidas. Esta herramienta nos permite minimizar el consumo de memoria y la sobrecarga de administración de memoria de las aplicaciones GPU sin necesidad de reescribirlas. Posteriormente, analizamos el tiempo de los algoritmos de control ejecutados en GPUs embebidas e identificamos técnicas para lograr un comportamiento de tiempo real aceptable. Finalmente, evaluamos las herramientas de diseño basadas en modelos en términos de integración con hardware GPU y generación de código GPU, y proponemos mejoras para el código GPU generado por las herramientas basadas en modelos. Luego, presentamos una herramienta de transformación de código fuente para aplicar automáticamente al código generado las mejoras propuestas.Postprint (published version

Simple, safe, and efficient memory management using linear pointers

Efficient and safe memory management is a hard problem. Garbage collection promises automatic memory management but comes with the cost of increased memory footprint, reduced parallelism in multi-threaded programs, unpredictable pause time, and intricate tuning parameters balancing the program's workload and designated memory usage in order for an application to perform reasonably well. Existing research mitigates the above problems to some extent, but programmer error could still cause memory leak by erroneously keeping memory references when they are no longer needed. We need a methodology for programmers to become resource aware, so that efficient, scalable, predictable and high performance programs may be written without the fear of resource leak. Linear logic has been recognized as the formalism of choice for resource tracking. It requires explicit introduction and elimination of resources and guarantees that a resource cannot be implicitly shared or abandoned, hence must be linear. Early languages based on linear logic focused on Curry-Howard correspondence. They began by limiting the expressive powers of the language and then reintroduced them by allowing controlled sharing which is necessary for recursive functions. However, only by deviating from Curry-Howard correspondence could later development actually address programming errors in resource usage. The contribution of this dissertation is a simple, safe, and efficient approach introducing linear resource ownership semantics into C++ (which is still a widely used language after 30 years since inception) through linear pointer, a smart pointer inspired by linear logic. By implementing various linear data structures and a parallel, multi-threaded memory allocator based on these data structures, this work shows that linear pointer is practical and efficient in the real world, and that it is possible to build a memory management stack that is entirely leak free. The dissertation offers some closing remarks on the difficulties a formal system would encounter when reasoning about a concurrent linear data algorithm, and what might be done to solve these problems

Some Experiences on Dynamic Memory Management Refinement at System-Level for Multimedia Applications

Nowadays, 3D multimedia applications have grown rapidly in number and consist of complex systems (e.g. 3D graphical processing or games) that process extensive amounts of data to create 3D images and results. This produces highcost and high-power consumption systems whereas a superior portability demands cheap and low-power consumption ones. In these multimedia applications, the dynamic memory subsystem is currently one of the main sources of power consumption and its inattentive management can affect severely the performance and power consumption of the whole system. In this paper, we illustrate a new system-level method to explore and refine the dynamic memory management of multimedia systems on current typical case studies, i.e. a relatively new 3D image reconstruction system and a 3D simulation game. This method is based on an analysis of the access pattern, amount of memory used and power consumption estimations. With this information, a phasewise exploration and refinement flow is used to optimize the system at the different phases of its hardware-oriented design process. As the results in the case studies show, our system-level method achieves great improvements in memory footprint, power consumption and performance for multimedia applications

Power Aware Tuning of Dynamic Memory Management for Embedded Real-Time Multimedia Applications

In the near future, portable embedded devices must run multimedia applications with enormous computational requirements at low energy consumption. These applications demand extensive memory footprint and must rely on dynamic memory due to the unpredictability of input data (e.g. 3D streams features) and system behaviour (e.g. variable number of applications running concurrently). Within this context, the dynamic memory subsystem is one of the main sources of power consumption and embedded systems have very limited batteries to provide efficient general-purpose dynamic memory management. As a result, consistent design methodologies that can efficiently tackle the complex dynamic memory behaviour of these new applications for low power embedded systems are in great need. In this paper we propose a step-wise system-level approach that allows the design of platform-specific dynamic memory management mechanisms with low power consumption for such kind of dynamic applications. The experimental results in reallife case studies show that our approach improves power consumption up to 89% over current state-of-the-art dynamic memory managers for complex applications

STATIC ENFORCEMENT OF TERMINATION-SENSITIVE NONINTERFERENCE USING THE C++ TEMPLATE TYPE SYSTEM

A side channel is an observable attribute of program execution other than explicit communication, e.g., power usage, execution time, or page fault patterns. A side-channel attack occurs when a malicious adversary observes program secrets through a side channel. This dissertation introduces Covert C++, a library which uses template metaprogramming to superimpose a security-type system on top of C++’s existing type system. Covert C++ enforces an information-flow policy that prevents secret data from influencing program control flow and memory access patterns, thus obviating side-channel leaks. Formally, Covert C++ can facilitate an extended definition of the classical noninterference property, broadened to also cover the dynamic execution property of memory-trace obliviousness. This solution does not require any modifications to the compiler, linker, or C++ standard. To verify that these security properties can be preserved by the compiler (i.e., by compiler optimizations), this dissertation introduces the Noninterference Verification Tool (NVT). The NVT employs a novel dynamic analysis technique which combines input fuzzing with dynamic memory tracing. Specifically, the NVT detects when secret data influences a program’s memory trace, i.e., the sequence of instruction fetches and data accesses. Moreover, the NVT signals when a program leaks secret data to a publicly-observable storage channel. The Covert C++ library and the NVT are two components of the broader Covert C++ toolchain. The toolchain also provides a collection of refactoring tools to interactively transform legacy C or C++ code into Covert C++ code. Finally, the dissertation introduces libOblivious, a library to facilitate high-performance memory-trace oblivious computation with Covert C++

Improving locality with dynamic memory allocation

Dynamic memory allocators are a determining factor of an application's performanceand have the opportunity to improve a major performance bottleneck ontoday's computer hardware: data locality. To approach this problem, a memoryallocator must rst oer strategies that allow the locality problem to be addressed.However, while focusing on locality, an allocator must also not ignore the existing constraintsof allocation speed and fragmentation, which further complicate its design. Inorder for a locality improving technique to be successfully employed in today's largecode applications, its integration needs to be automatic, without user intervention.The alternative, manual integration, is not a tractable solution.In this dissertation we develop three novel memory allocators that explore dierentallocation strategies that enhance an application's locality. We conduct the rststudy that shows that allocation speed, fragmentation and locality improving goalsare antagonistic. We develop an automatic method that supplies allocation hintsfrom C++ STL containers to their allocators. This method allows applications tobenet from locality improving techniques at the cost of a simple re-compilation. Weconduct the rst study that quanties the eect of allocation hints on performance,and show that an allocator with high locality of reference can be as competitive asone using an application's spatial feedback.To further allow dynamic memory allocation to improve an application's performance,new and non-traditional strategies need be explored. We develop a generic software tool that allows users to examine unconventional strategies. The tool allowsusers not only to focus on allocation strategies rather than their implementation, butalso to compare and contrast various approaches