Real-time analysis of MPI programs for NoC-based many-cores using time division multiplexing

Worst-case execution time (WCET) analysis is crucial for designing hard real-time systems. While the WCET of tasks in a single core system can be upper bounded in isolation, the tasks in a many-core system are subject to shared memory interferences which impose high overestimation of the WCET bounds. However, many-core-based massively parallel applications will enter the area of real-time systems in the years ahead. Explicit message-passing and a clear separation of computation and communication facilitates WCET analysis for those programs. A standard programming model for message-based communication is the message passing interface (MPI). It provides an application independent interface for different standard communication operations (e.g. broadcast, gather, ...). Thereby, it uses efficient communication patterns with deterministic behaviour. In applying these known structures, we target to provide a WCET analysis for communication that is reusable for different applications if the communication is executed on the same underlying platform. Hence, the analysis must be performed once per hardware platform and can be reused afterwards with only adapting several parameters such as the number of nodes participating in that communication. Typically, the processing elements of many-core platforms are connected via a Network-on-Chip (NoC) and apply techniques such as time-division multiplexing (TDM) to provide guaranteed services for the network. Hence, the hardware and the applied technique for guaranteed service needs to facilitate this reusability of the analysis as well. In this work we review different general-purpose TDM schedules that enable a WCET approximation independent of the placement of tasks on processing elements of a many-core which uses a NoC with torus topology. Furthermore, we provide two new schedules that show a similar performance as the state-of-the-art schedules but additionally serve situations where the presented state-of-the-art schedules perform poorly. Based on these schedules a procedure for the WCET analysis of the communication patterns used in MPI is proposed. Finally, we show how to apply the results of the analysis to calculate the WCET upper bound for a complete MPI program. Detailed insights in the performance of the applied TDM schedules are provided by comparing the schedules to each other in terms of timing. Additionally, we discuss the exhibited timing of the general-purpose schedules compared to a state-of-the-art application specific TDM schedule to put in relation both types of schedules. We apply the proposed procedure to several standard types of communication provided in MPI and compare different patterns that are used to implement a specific communication. Our evaluation investigates the communications’ building blocks of the timing bounds and shows the tremendous impact of choosing the appropriate communication pattern. Finally, a case study demonstrates the application of the presented procedure to a complete MPI program. With the method proposed in this work it is possible to perform a reusable WCET timing analysis for the communication in a NoC that is independent of the placement of tasks on the chip. Moreover, as the applied schedules are not optimized for a specific application but can be used for all applications in the same way, there are only marginal changes in the timing of the communication when the software is adapted or updated. Thus, there is no need to perform the timing analysis from scratch in such cases

Genetic Algorithm Modeling with GPU Parallel Computing Technology

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability.Comment: 11 pages, 2 figures, refereed proceedings; Neural Nets and Surroundings, Proceedings of 22nd Italian Workshop on Neural Nets, WIRN 2012; Smart Innovation, Systems and Technologies, Vol. 19, Springe

Analysing Astronomy Algorithms for GPUs and Beyond

Astronomy depends on ever increasing computing power. Processor clock-rates have plateaued, and increased performance is now appearing in the form of additional processor cores on a single chip. This poses significant challenges to the astronomy software community. Graphics Processing Units (GPUs), now capable of general-purpose computation, exemplify both the difficult learning-curve and the significant speedups exhibited by massively-parallel hardware architectures. We present a generalised approach to tackling this paradigm shift, based on the analysis of algorithms. We describe a small collection of foundation algorithms relevant to astronomy and explain how they may be used to ease the transition to massively-parallel computing architectures. We demonstrate the effectiveness of our approach by applying it to four well-known astronomy problems: Hogbom CLEAN, inverse ray-shooting for gravitational lensing, pulsar dedispersion and volume rendering. Algorithms with well-defined memory access patterns and high arithmetic intensity stand to receive the greatest performance boost from massively-parallel architectures, while those that involve a significant amount of decision-making may struggle to take advantage of the available processing power.Comment: 10 pages, 3 figures, accepted for publication in MNRA

Teaching Parallel Programming Using Java

This paper presents an overview of the "Applied Parallel Computing" course taught to final year Software Engineering undergraduate students in Spring 2014 at NUST, Pakistan. The main objective of the course was to introduce practical parallel programming tools and techniques for shared and distributed memory concurrent systems. A unique aspect of the course was that Java was used as the principle programming language. The course was divided into three sections. The first section covered parallel programming techniques for shared memory systems that include multicore and Symmetric Multi-Processor (SMP) systems. In this section, Java threads was taught as a viable programming API for such systems. The second section was dedicated to parallel programming tools meant for distributed memory systems including clusters and network of computers. We used MPJ Express-a Java MPI library-for conducting programming assignments and lab work for this section. The third and the final section covered advanced topics including the MapReduce programming model using Hadoop and the General Purpose Computing on Graphics Processing Units (GPGPU).Comment: 8 Pages, 6 figures, MPJ Express, MPI Java, Teaching Parallel Programmin

Best practices for HPM-assisted performance engineering on modern multicore processors

Many tools and libraries employ hardware performance monitoring (HPM) on modern processors, and using this data for performance assessment and as a starting point for code optimizations is very popular. However, such data is only useful if it is interpreted with care, and if the right metrics are chosen for the right purpose. We demonstrate the sensible use of hardware performance counters in the context of a structured performance engineering approach for applications in computational science. Typical performance patterns and their respective metric signatures are defined, and some of them are illustrated using case studies. Although these generic concepts do not depend on specific tools or environments, we restrict ourselves to modern x86-based multicore processors and use the likwid-perfctr tool under the Linux OS.Comment: 10 pages, 2 figure