Thread Checker Research Articles

Detecting Memory Related Errors using a Valgrind Tool Suite and Detecting Data Races using Thread Sanitizer Clang

It is important for a programmer to detect data races when a programming is done under concurrent systems or using shared memory or parallel programming. Many researchers are implementing their applications using the parallel programming and even for simple c programs ,they are not much concentrating on memory issues,accuracy of a result obtained, speed. It is important and one must take care it. There may found data races in their application code which has to be resolve and for that some data race detectors are required. There is also a need of detecting the memory related problems that may occur with shared memory. As a result of such memory related bugs and data races, the system may slow down the speed of the execution of a program .To improve the speed,the valgrind tool suite is used for memory related problems and ThreadSanitizer is used for detecting data races and some authors used Intel Thread Checker,ADAT,on-the-fly techniques for detecting them. Some tools are reducing the false positives but removing all false positives. Valgrind is providing a number of debugging and profiling tools that helps in improving the accuracy and to enhance the performance of system. This paper is demonstrating and showing the different results obtained by using the mentioned tool suite. The available tools in valgrind tool suite helps a programmer by displaying appropriate messages if any memory related errors and based on these messages the appropriate action can be taken by the programmer. And next time when user start writing similar type of application code, tries to avoid such errors and this makes our programs faster and avoids memory leaks. The memory leaks may happen when the programmer tries to write or read or access any location of an array beyond its bound. There are different categories of memory leaks. From the existing categories the two mostly used are definitely lost and probably lost and the result is showing both the memory leaks. The result through the stack trace tells where the memory leak is allocated. The output can also be store in a log file or simply we can display through appropriate error messages. This paper is showing some of the error messages and demonstrated through some programming example and related error messages using valgrind tool suite. Keywords: Memory leak; valgrind tool suite; profiling tools; stack trace; ThreadSanitizer; heap memory; Intel Thread Checker; false positive;

Comparison and Analysis of Parallel Computing Performance Using OpenMP and MPI

The developments of multi-core technology have induced big challenges to software structures. To take full advantages of the performance enhancements offered by new multi-core hardware, software programming models have made a great shift from sequential programming to parallel programming. OpenMP (Open Multi-Processing) and MPI (Message Passing Interface), as the most common parallel programming models, can provide different performance characteristics of parallelism in different cases. This paper intends to compare and analyze the parallel computing ability between OpenMP and MPI, and then some proposals are provided in parallel programming. The processing tools used include Intel VTune Performance Analyzer and Intel Thread Checker. The find- ings indicate that OpenMP is in favor of implementation and provides good performance in shared memory systems , and that MPI is propitious to programming models which require nodes performing a large number of tasks and little commu- nications in processes.

VORD: A Versatile On-the-fly Race Detection Tool in OpenMP Programs

Shared-memory based parallel programming with OpenMP and Posix-thread APIs becomes more common to fully take advantage of multiprocessor computing environments. One of the critical risks in multithreaded programming is data races that are hard to debug and greatly damaging to parallel applications if they are uncaught. Although ample effort has been made in building specialized data race detection techniques, the state of art tools such as the Intel Thread Checker still have various functionality and performance problems. In this paper, we present a Versatile On-the-fly Race Detection (VORD) tool that provides an agile, efficient, and scalable race detection environment for various parallel programming models. VORD can automatically construct an empirically optimal set of race engines by utilizing classification and adaptation mechanisms. A Race-Detection Classification (RDC) table is created to categorize adequate engines in the aspect of labeling, detecting, and filtering. An Engine Code Property Selector (ECPS) uses the RDC table to adapt optimal engines for the given target programming models. In addition to RDC and ECPS, we have also implemented an OpenMP parser and a source instrumentor. The functionality and efficiency of VORD were compared with those of the Intel Thread Checker by using a set of OpenMP based kernel programs. The experimental results show that VORD can detect data races in more challenging programming models such as nested thread and synchronization models, and can achieve a couple of orders of a magnitude faster processing time than the Intel Thread Checker in the large parallel programs.

A Study of Parallelization and Performance Optimizations Based on OpenMP

The primary consequence of the transition to multicore processors is that applications will increasingly need to be parallelized to improve application's throughput, responsiveness and latency. Multithreading is becoming increasingly important for modern programming. Unfortunately, parallel programming is no doubt much more tedious and error-prone than serial programming. Although modern compilers can manage threads well, but in practice, synchronization errors (such as: data race errors, deadlocks) required careful management and good optimization method. This paper presents a preliminary study of the usability of the Intel threading tools for multicore programming. This work compare performance of a single threaded application with multithreaded applications, use tools called Intel® VTune Performance Analyzer, Intel® Thread Checker and OpenMP to efficiently optimize multithreaded applications.

Data Race Detection with Minimal Hardware Support

This article presents AccessedBefore (AccB), an algorithm and its associated minimal hardware support to detect data races, and compares it with two widely known and used commercial tools: Helgrind, the data race detection tool included in the general purpose memory checking suite Valgrind, and Intel Thread Checker, now shipped as part of Intel Thread Inspector. It provides a performance overhead evaluation using current workloads, along with an analysis of AccB's scalability with the number of threads and workload input set size. It demonstrates that AccB is in the range of 2× to 11× faster than these two tools. Finally, it shows the complete proof that AccB is complete in that, for every static data race present in a program, there exists an instruction interleaving that would expose this data race such that AccB can detect it. © 2013 The Author 2013. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

MPI Correctness Checking for OpenMP/MPI Applications

The MPI interface is the de-facto standard for message passing applications, but it is also complex and defines several usage patterns as erroneous. A current trend is the investigation of hybrid programming techniques that use MPI processes and multiple threads per process. As a result, more and more MPI implementations support multi-threading, which are restricted by several rules of the MPI standard. In order to support developers of hybrid MPI applications, we present extensions to the MPI correctness checking tool Marmot. Basic extensions make it aware of OpenMP multi-threading, while further ones add new correctness checks. As a result, it is possible to detect errors that actually occur in a run with Marmot. However, some errors only occur for certain execution orders, thus, we present a novel approach using artificial data races, which allows us to employ thread checking tools, e.g., Intel Thread Checker, to detect MPI usage errors.