Bounding Worst-Case Data Cache Behavior by Analytically Deriving Cache Reference Patterns

While caches have become invaluable for higher-end architectures due to their ability to hide, in part, the gap between processor speed and memory access times, caches (and particularly data caches) limit the timing predictability for data accesses that may reside in memory or in cache. This is a significant problem for real-time systems. The objective our work is to provide accurate predictions of data cache behavior of scalar and non-scalar references whose reference patterns are known at compile time. Such knowledge about cache behavior provides the basis for significant improvements in bounding the worst-case execution time (WCET) of real-time programs, particularly for hard-to-analyze data caches. We exploit the power of the Cache Miss Equations (CME) framework but lift a number of limitations of traditional CME to generalize the analysis to more arbitrary programs. We further devised a transformation, coined “forced” loop fusion, which facilitates the analysis across sequential loops. Our contributions result in exact data cache reference patterns — in contrast to approximate cache miss behavior of prior work. Experimental results indicate improvements on the accuracy of worst-case data cache behavior up to two orders of magnitude over the original approach. In fact, our results closely bound and sometimes even exactly match those obtained by trace-driven simulation for worst-case inputs. The resulting WCET bounds of timing analysis confirm these findings in terms of providing tight bounds. Overall, our contributions lift analytical approaches to predict data cache behavior to a level suitable for efficient static timing analysis and, subsequently, real-time schedulability of tasks with predictable WCET

ImageSpirit: Verbal Guided Image Parsing

Humans describe images in terms of nouns and adjectives while algorithms operate on images represented as sets of pixels. Bridging this gap between how humans would like to access images versus their typical representation is the goal of image parsing, which involves assigning object and attribute labels to pixel. In this paper we propose treating nouns as object labels and adjectives as visual attribute labels. This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images. We propose an efficient (interactive time) solution. Using the extracted labels as handles, our system empowers a user to verbally refine the results. This enables hands-free parsing of an image into pixel-wise object/attribute labels that correspond to human semantics. Verbally selecting objects of interests enables a novel and natural interaction modality that can possibly be used to interact with new generation devices (e.g. smart phones, Google Glass, living room devices). We demonstrate our system on a large number of real-world images with varying complexity. To help understand the tradeoffs compared to traditional mouse based interactions, results are reported for both a large scale quantitative evaluation and a user study.Comment: http://mmcheng.net/imagespirit

Loop Bound Analysis based on a Combination of Program Slicing, Abstract Interpretation, and Invariant Analysis

Static Worst-Case Execution Time (WCET) analysis is a technique to derive upper bounds for the execution times of programs. Such bounds are crucial when designing and verifying real-time systems. A key component for static derivation of precise WCET estimates is upper bounds on the number of times different loops can be iterated. In this paper we present an approach for deriving upper loop bounds based on a combination of standard program analysis techniques. The idea is to bound the number of different states in the loop which can influence the exit conditions. Given that the loop terminates, this number provides an upper loop bound. An algorithm based on the approach has been implemented in our WCET analysis tool SWEET. We evaluate the algorithm on a number of standard WCET benchmarks, giving evidence that it is capable to derive valid bounds for many types of loops

Program Semantics in Model-Based WCET Analysis: A State of the Art Perspective

Advanced design techniques of safety-critical applications use specialized development model based methods. Under this setting, the application exists at several levels of description, as the result of a sequence of transformations. On the positive side, the application is developed in a systematic way, while on the negative side, its high-level semantics may be obfuscated when represented at the lower levels. The application should provide certain functional and non-functional guarantees. When the application is a hard real-time program, such guarantees could be deadlines, thus making the computation of worst-case execution time (WCET) bounds mandatory. This paper overviews, in the context of WCET analysis, what are the existing techniques to extract, express and exploit the program semantics along the model-based development workflow

TuBound - A Conceptually New Tool for Worst-Case Execution Time Analysis

TuBound is a conceptually new tool for the worst-case execution time (WCET) analysis of programs. A distinctive feature of TuBound is the seamless integration of a WCET analysis component and of a compiler in a uniform tool. TuBound enables the programmer to provide hints improving the precision of the WCET computation on the high-level program source code, while preserving the advantages of using an optimizing compiler and the accuracy of a WCET analysis performed on the low-level machine code. This way, TuBound ideally serves the needs of both the programmer and the WCET analysis by providing them the interface on the very abstraction level that is most appropriate and convenient to them. In this paper we present the system architecture of TuBound, discuss the internal work-flow of the tool, and report on first measurements using benchmarks from Maelardalen University. TuBound took also part in the WCET Tool Challenge 2008

Extreme scale parallel NBody algorithm with event driven constraint based execution model

Traditional scientific applications such as Computational Fluid Dynamics, Partial Differential Equations based numerical methods (like Finite Difference Methods, Finite Element Methods) achieve sufficient efficiency on state of the art high performance computing systems and have been widely studied / implemented using conventional programming models. For emerging application domains such as Graph applications scalability and efficiency is significantly constrained by the conventional systems and their supporting programming models. Furthermore technology trends like multicore, manycore, heterogeneous system architectures are introducing new challenges and possibilities. Emerging technologies are requiring a rethinking of approaches to more effectively expose the underlying parallelism to the applications and the end-users. This thesis explores the space of effective parallel execution of ephemeral graphs that are dynamically generated. The standard particle based simulation, solved using the Barnes-Hut algorithm is chosen to exemplify the dynamic workloads. In this thesis the workloads are expressed using sequential execution semantics, a conventional parallel programming model - shared memory semantics and semantics of an innovative execution model designed for efficient scalable performance towards Exascale computing called ParalleX. The main outcomes of this research are parallel processing of dynamic ephemeral workloads, enabling dynamic load balancing during runtime, and using advanced semantics for exposing parallelism in scaling constrained applications

Custom optimization algorithms for efficient hardware implementation

The focus is on real-time optimal decision making with application in advanced control systems. These computationally intensive schemes, which involve the repeated solution of (convex) optimization problems within a sampling interval, require more efficient computational methods than currently available for extending their application to highly dynamical systems and setups with resource-constrained embedded computing platforms. A range of techniques are proposed to exploit synergies between digital hardware, numerical analysis and algorithm design. These techniques build on top of parameterisable hardware code generation tools that generate VHDL code describing custom computing architectures for interior-point methods and a range of first-order constrained optimization methods. Since memory limitations are often important in embedded implementations we develop a custom storage scheme for KKT matrices arising in interior-point methods for control, which reduces memory requirements significantly and prevents I/O bandwidth limitations from affecting the performance in our implementations. To take advantage of the trend towards parallel computing architectures and to exploit the special characteristics of our custom architectures we propose several high-level parallel optimal control schemes that can reduce computation time. A novel optimization formulation was devised for reducing the computational effort in solving certain problems independent of the computing platform used. In order to be able to solve optimization problems in fixed-point arithmetic, which is significantly more resource-efficient than floating-point, tailored linear algebra algorithms were developed for solving the linear systems that form the computational bottleneck in many optimization methods. These methods come with guarantees for reliable operation. We also provide finite-precision error analysis for fixed-point implementations of first-order methods that can be used to minimize the use of resources while meeting accuracy specifications. The suggested techniques are demonstrated on several practical examples, including a hardware-in-the-loop setup for optimization-based control of a large airliner.Open Acces

Badger: Complexity Analysis with Fuzzing and Symbolic Execution

Hybrid testing approaches that involve fuzz testing and symbolic execution have shown promising results in achieving high code coverage, uncovering subtle errors and vulnerabilities in a variety of software applications. In this paper we describe Badger - a new hybrid approach for complexity analysis, with the goal of discovering vulnerabilities which occur when the worst-case time or space complexity of an application is significantly higher than the average case. Badger uses fuzz testing to generate a diverse set of inputs that aim to increase not only coverage but also a resource-related cost associated with each path. Since fuzzing may fail to execute deep program paths due to its limited knowledge about the conditions that influence these paths, we complement the analysis with a symbolic execution, which is also customized to search for paths that increase the resource-related cost. Symbolic execution is particularly good at generating inputs that satisfy various program conditions but by itself suffers from path explosion. Therefore, Badger uses fuzzing and symbolic execution in tandem, to leverage their benefits and overcome their weaknesses. We implemented our approach for the analysis of Java programs, based on Kelinci and Symbolic PathFinder. We evaluated Badger on Java applications, showing that our approach is significantly faster in generating worst-case executions compared to fuzzing or symbolic execution on their own