A compile-time memory-reuse scheme for concurrent logic programs

In this paper a formal model for program dependence analysis of concurrent logic programs is proposed with the following contributions. First, two languageindependent program representations are presented for explicitly representing control flows and/or data flows in a concurrent logic program. Then based on these representations, program dependences between literals in concurrent logic programs are defined formally, and a dependence-based program representation named the Literal Dependence Net (LDN) is presented for explicitly representing primary program dependences in a concurrent logic program. Finally, as applications of the LDNs, some important software engineering activities including program slicing, debugging, testing, complexity measurement, and maintenance are discussed in a programming environment for concurrent logic programs. 1 Introduction It is well-known that programming environments are very important for the acceptance of programming languages. Unfortunately, rece...

A secure software demands effective techniques for vulnerability detection during its development cycle. The practice of detecting security flaws before the deployment phase eliminates the risks that vulnerabilities may impose for the company. Static analysis and dynamic analysis techniques offer two complimentary approaches for checking vulnerabilities. Static analysis involves the scanning of source code or binary eliminating the need of executing it. This approach is fast and has no run time overhead. However, static analysis are quite imprecise and generate huge false positives and false negatives. On the other hand, dynamic analysis involves the running of the software. The problem of false positives and negatives is less in case of dynamic analysis because they analyze by running the test cases. But this approach requires large number of test cases to ensure a certain confidence level in detecting security bugs. This paper describes a methodology which integrates the two approaches in a complimentary manner. It adopts the strengths of the two and eliminates their weaknesses. We are currently dealing with buffer overflow vulnerability with pointer aliasing. However the idea can be extended to other vulnerabilities also for e.g memory related errors, race conditions( Time of Check to Time to Use vulnerability), dangling pointer vulnerability, integer errors etc.

OASIS is a formal approach for the specification of object oriented conceptual models. In OASIS conceptual schemas of information systems are represented as societies of interacting concurrent objects. Animating such models in order to validate the specification of information systems is a topic of interest in requirements engineering. Thus a basic execution model for OASIS specifications has been developed. Concurrent Logic Programming is a suitable paradigm for distributed computation allowing a natural representation of concurrence. Using Concurrent Logic Programming, OASIS specifications are animated according to OASIS execution model. In this work, we show how OASIS concepts are directly mapped into concurrent logic programming concepts. To illustrate our ideas, an example of a bank account codified in KL1 is given and parts of the program that animates its corresponding OASIS specification are shown. This work has been developed in the context of a CASE tool supporting the OASIS approach. Our aim is to build a module for animation and validation of specifications. A preliminary version of this module is presented.

AbstractjHISC is an object-oriented processor for embedded system aiming at accelerating Java execution by hardware approach. Garbage collection is one of the critical tasks in a Java Virtual Machine. In this paper, we have conduct a study of dynamic object allocation and garbage collection behavior of Java program based on SPECjvm 98 benchmark suite and MIDP applications for mobile phones. Life, size, and reference count distribution of Java objects are measured. We found most Java objects die very young, small in size and have small number reference counts. Reference counting object cache with hardware write barrier and object allocator is proposed to provide the hardware concurrent garbage collection for small size objects in jHISC. Hardware support on write barrier greatly reduces the overhead to perform the reference count update. The reference counting collector reclaims the memory occupied by object immediately after the object become garbage. The hardware allocator provides a constant time object allocation. From the investigation, over half of Java objects can be garbage collected by the object cache that makes it unnecessary for these objects to copy to the main memory.KeywordsGarbage CollectionCache LineBenchmark SuiteJava Virtual MachineGarbage CollectorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Tracing and reference counting are uniformly viewed as being fundamentally different approaches to garbage collection that possess very distinct performance properties. We have implemented high-performance collectors of both types, and in the process observed that the more we optimized them, the more similarly they behaved - that they seem to share some deep structure. We present a formulation of the two algorithms that shows that they are in fact duals of each other. Intuitively, the difference is that tracing operates on live objects, or "matter", while reference counting operates on dead objects, or "anti-matter". For every operation performed by the tracing collector, there is a precisely corresponding anti-operation performed by the reference counting collector. Using this framework, we show that all high-performance collectors (for example, deferred reference counting and generational collection) are in fact hybrids of tracing and reference counting. We develop a uniform cost-model for the collectors to quantify the trade-offs that result from choosing different hybridizations of tracing and reference counting. This allows the correct scheme to be selected based on system performance requirements and the expected properties of the target application.

Reversible functional languages have been proposed that use patterns symmetrically for matching and building data: A pattern used on the left-hand side of a function rule takes apart a data structure, and a pattern used on the right-hand side of a rule builds a data structure. When calling a function in reverse, the meaning of patterns are reversed, so patterns on the right-hand side take apart data and patterns on the left-hand side build data. If using a pattern to build data creates a node with exactly one reference, a node that is taken apart must by symmetry also have exactly one reference. This implies linearity: A node that is built or taken apart has exactly one incoming reference. A recursive function can take apart one data structure while building two or more new copies, allowing multiple uses of values, but the new copies will also have one reference each. Linearity makes garbage collection simple: Whenever a node is taken apart by pattern matching, it is freed. The cost is that multiple uses of a value require deep copies. The reason for the linearity restriction is the symmetry between constructing and deconstructing nodes: Since constructing a node creates exactly one reference to this node, the inverse can only be applied if the reference count is exactly one. We can overcome this limitation if constructing a node can return a node with multiple references. We achieve this through maximal sharing: If a newly constructed node is identical to an already existing node, we return a pointer to the existing node (increasing its reference count) instead of allocating a new node with reference count one. This allows multiple references to nodes while retaining the symmetry of pattern matching and construction, and it allows values to be used multiple times without making deep copies. To avoid searching the entire heap for an identical node, we use hash-consing to restrict the search to a small segment of the heap. We estimate how large this segment needs to be to give a low probability of allocation failure with acceptable utilisation and support this estimate with experiments. We sketch how a functional program can be translated to use the memory manager, and we test the memory manager both with artificial test programs and a hand-compiled functional program.

As the size of embedded system software increase bigger and bigger, and it`s complexity is grower and grower, the usage of dynamic memory management scheme such collector also has been increased. Using the garbage collector, however, inherently lead us performance degradation. In order to resolve this kind of performance problem in the Java based embedded system. we introduce an explicit dynamic memory free method to the automated dynamic memory management environment. which can be performed by a programmer. In the worst case, the prosed scheme shows the same performance as the case of that only garbage collector is working, since the unclaimed garbage objects will eventually be collected later by the garbage collector. In the best case. our method is free from any runtime overhead because the applications can be implemented without any intervention of the garbage collector. Although the proposed method can be facilitated with all the existing garbage collection algorithms, it shows an outperform in the case of mark-and-sweep algorithm.

A problem which confronts the developers of concurrent logic programming (CLP) systems concerns the design of the programming environment, particularly the provision of debugging tools. Debugging tools are useful for many activities besides identifying bugs: they can help in program testing and demonstration, in software experiments, and in teaching the language semantics. For CLP languages the questions of what debugging tools should be provided, and of how they should be used, are still open. Although the languages are closely related to other logic programming and concurrent programming languages, they are sufficiently different that new debugging techniques are required. This paper describes a primarily channel-oriented debugging methodology and a set of debugging tools that we have developed in the light of our experience in using and teaching the CLP language Parlog. With these tools a programmer can test a program by observing communication on channels, opening up a process to examine the activity on internal channels and, if necessary, to check in detail the execution steps of a process. Almost all of the ideas, which are equally applicable to other CLP languages, have been implemented in two commercial Parlog systems. As well as fulfilling the needs of existing users of CLP languages, we believe that the tools emphasise the attractions of CLP as a concurrent programming paradigm, since they are made possible by the unique attributes of CLP.

Defending against Return-Oriented Programming attacks based on return instruction using static analysis and binary patch techniques

We have implemented Kima, an automated error correction system for concurrent logic programs. Kima corrects near-misses such as wrong variable occurrences in the absence of explicit declarations of program properties. Strong moding/typing and constraint-based analysis are turning out to play fundamental roles in debugging concurrent logic programs as well as in establishing the consistency of communication protocols and data types. Mode/type analysis of Moded Flat GHC is a constraint satisfaction problem with many simple mode/type constraints, and can be solved efficiently. We proposed a simple and efficient technique which, given a non-well-moded/typed program, diagnoses the “reasons” of inconsistency by finding minimal inconsistent subsets of mode/type constraints. Since each constraint keeps track of the symbol occurrence in the program, a minimal subset also tells possible sources of program errors. Kima realizes automated correction by replacing symbol occurrences around the possible sources and recalculating modes and types of the rewritten programs systematically. As long as bugs are near-misses, Kima proposes a rather small number of alternatives that include an intended program. Search space is kept small because the minimal subset confines possible sources of errors in advance. This paper presents the basic algorithm and various optimization techniques implemented in Kima, and then discusses its effectiveness based on quantitative experiments.

Malware classification plays an important role in tracing the attack sources of computer security. However, existing static analysis methods are fast in classification, but they are inefficient in some malware using packing and obfuscation techniques; the dynamic analysis methods have better universality for packing and obfuscation, but they will cause excessive classification cost. To overcome these shortcomings, in this paper, we propose a classification system Malscore based on the probability scoring and machine learning, which sets the probability threshold to concatenate static analysis (called Phase 1) and dynamic analysis (called Phase 2). The convolutional neural networks with spatial pyramid pooling were used to analyze the grayscale images (static features) in Phase 1, and the variable n-grams and machine learning were used to analyze the native API call sequences (dynamic features) in Phase 2. Malscore combined static analysis with dynamic analysis not only accelerated the static analysis process by taking advantage of the CNN in image recognition but also appeared to be more resilient to obfuscation by the dynamic analysis. Different from other static and dynamic analysis techniques, when malware is detected, due to the fact that malware will most likely be labeled only by static analysis, we could reduce the overheads by dynamically analyzing a few malware that has less obvious features or greater confusion in static analysis. We performed experiments on 174607 malware samples from 63 malware families. The result showed that Malscore achieved 98.82% accuracy for malware classification. Furthermore, Malscore was compared with the method of using static and dynamic analysis. The preprocessing and test time represented a reduction of 59.58% and 61.70%, respectively.

With the advent of new flash-based memory technologies with ultra-low latency, directly applying inline data deduplication in flash-based storage devices can degrade the system performance since key deduplication operations lie on the shortened critical write path of such devices. To address the problem, we propose a Content-Aware Garbage Collection scheme (CAGC), which embeds the data deduplication into the data movement workflow of the Garbage Collection (GC) process in ultra-low latency flash-based SSDs. By parallelizing the operations of valid data pages migration, hash computing and flash block erase, the deduplication-induced performance overhead is alleviated and redundant page writes during the GC period are eliminated. To further reduce data writes and write amplification during GC, CAGC separates and stores data pages in different regions based on their reference counts. The performance evaluation of our CAGC prototype implemented in FlashSim shows that CAGC significantly reduces the number of flash blocks erased and data pages migrated during GC, leading to improved user I/O performance and reliability of ultra-low latency flash-based SSDs.

The use of types to deal with access capabilities of program entities is becoming increasingly popular.In concurrent logic programming, the first attempt was made in Moded Flat GHC in 1990, which gave polarity structures (modes) to every variable occurrence and every predicate argument. Strong moding turned out to play fundamental rôles in programming, implementation and the in-depthunderstanding of constraint-based concurrent computation.The moding principle guarantees that each variable is written only once and encourages capability-conscious programming. Furthermore, it gives less generic modes to programs that discard or duplicate data, thus providing the view of “data as resources.” A simple linearity system built upon the mode system distinguishes variables read only once from those read possibly many times, enabling compile-time garbage collection. Compared to linear types studied in other programming paradigms, the primary issue in constraint-based concurrency has been to deal with logical variables and highly non-strict data structures they induce.In this paper, we put our resource-consciousness one step forward and consider a class of ‘ecological’ programs which recycle or return all the resources given to them while allowing concurrent reading of data structures via controlled aliasing. This completely recyclic subset enforces us to think more about resources, but the resulting programs enjoy high symmetry which we believe has more than aesthetic implications to our programming practice in general.The type system supporting recyclic concurrent programming gives a [−1, +1] capability to each occurrence of variable and function symbols (constructors), where positive/negative values mean read/write capabilities, respectively, and fractions mean non-exclusive read/write paths. The capabilities are intended to be statically checked or reconstructed so that one can tell the polarity and exclusiveness of each piece of information handled by concurrent processes. The capability type system refines and integrates the mode system and the linearity system for Moded Flat GHC. Its arithmetic formulation contributes to the simplicity.The execution of a recyclic program proceeds so that every variable has zero-sum capability and the resources (i.e., constructors weighted by their capabilities) a process absorbs match the resources it emits. Constructors accessed by a process withan exclusive read capability can be reused for other purposes.The first half of this paper is devoted to a tutorial introduction to constraint-based concurrency in the hope that it will encourage crossfertilization of different concurrency formalisms.KeywordsLogic ProgramMode GraphLogical VariableMode ConstraintProgram ClauseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The authors have designed and implemented a modular Lisp interpreter for garbage collector evaluation. Data structures such as the list store, the hash table, the symbol table and the stacks were kept in separate modules with only the routines needed to handle them. Routines in other modules may only read and manipulate these data structures through the specified routines. Using this approach, when a garbage collector strategy or method of storing list cells is changed only the store module need be modified. Test programs and measurement strategies were designed for evaluating garbage collector performance in a virtual memory environment. Three garbage collectors have been coded and tested. They are a conventional link reversal marking garbage collector, a conventional recopying garbage collector, and a garbage collector integrated with the system swapper. The implementations are described and their performances are compared. >

Despite some clear advantages and recent advances, reference counting remains a poor cousin to high-performance tracing garbage collectors. The advantages of reference counting include a) immediacy of reclamation, b) incrementality, and c) local scope of its operations. After decades of languishing with hopelessly bad performance, recent work narrowed the gap between reference counting and the fastest tracing collectors to within 10%. Though a major advance, this gap remains a substantial barrier to adoption in performance-conscious application domains. Our work identifies heap organization as the principal source of the remaining performance gap. We present the design, implementation, and analysis of a new collector, RC Immix, that replaces reference counting's traditional free-list heap organization with the line and block heap structure introduced by the Immix collector. The key innovations of RC Immix are 1) to combine traditional reference counts with per-line live object counts to identify reusable memory and 2) to eliminate fragmentation by integrating copying with reference counting of new objects and with backup tracing cycle collection. In RC Immix, reference counting offers efficient collection and the line and block heap organization delivers excellent mutator locality and efficient allocation. With these advances, RC Immix closes the 10% performance gap, matching the performance of a highly tuned production generational collector. By removing the performance barrier, this work transforms reference counting into a serious alternative for meeting high performance objectives for garbage collected languages.