Performance measurement and analysis of PC based cluster server using SET of Architecture and modeling a scalable High performance cluster

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Optimizations and Cost Models for multi-core architectures: an approach based on parallel paradigms

The trend in modern microprocessor architectures is clear: multi-core chips are here to stay, and researchers expect multiprocessors with 128 to 1024 cores on a chip in some years. Yet the software community is slowly taking the path towards parallel programming: while some works target multi-cores, these are usually inherited from the previous tools for SMP architectures, and rarely exploit specific characteristics of multi-cores. But most important, current tools have no facilities to guarantee performance or portability among architectures. Our research group was one of the first to propose the structured parallel programming approach to solve the problem of performance portability and predictability. This has been successfully demonstrated years ago for distributed and shared memory multiprocessors, and we strongly believe that the same should be applied to multi-core architectures. The main problem with performance portability is that optimizations are effective only under specific conditions, making them dependent on both the specific program and the target architecture. For this reason in current parallel programming (in general, but especially with multi-cores) optimizations usually follows a try-and-decide approach: each one must be implemented and tested on the specific parallel program to understand its benefits. If we want to make a step forward and really achieve some form of performance portability, we require some kind of prediction of the expected performance of a program. The concept of performance modeling is quite old in the world of parallel programming; yet, in the last years, this kind of research saw small improvements: cost models to describe multi-cores are missing, mainly because of the increasing complexity of microarchitectures and the poor knowledge of specific implementation details of current processors. In the first part of this thesis we prove that the way of performance modeling is still feasible, by studying the Tilera TilePro64. The high number of cores on-chip in this processor (64) required the use of several innovative solutions, such as a complex interconnection network and the use of multiple memory interfaces per chip. For these features the TilePro64 can be considered an insight of what to expect in future multi-core processors. The availability of a cycle-accurate simulator and an extensive documentation allowed us to model the architecture, and in particular its memory subsystem, at the accuracy level required to compare optimizations In the second part, focused on optimizations, we cover one of the most important issue of multi-core architectures: the memory subsystem. In this area multi-core strongly differs in their structure w.r.t off-chip parallel architectures, both SMP and NUMA, thus opening new opportunities. In detail, we investigate the problem of data distribution over the memory controllers in several commercial multi-cores, and the efficient use of the cache coherency mechanisms offered by the TilePro64 processor. Finally, by using the performance model, we study different implementations, derived from the previous optimizations, of a simple test-case application. We are able to predict the best version using only profiled data from a sequential execution. The accuracy of the model has been verified by experimentally comparing the implementations on the real architecture, giving results within 1 − 2% of accuracy

Structured Parallel Programming and Cache Coherence in Multicore Architectures

It is clear that multicore processors have become the building blocks of today’s high-performance computing platforms. The advent of massively parallel single-chip microprocessors further emphasizes the gap that exists between parallel architectures and parallel programming maturity. Our research group, starting from the experiences on distributed and shared memory multiprocessor, was one of the first to propose a Structured Parallel Programming approach to bridge this gap. In this scenario, one of the biggest problems is that an application’s performance is often affected by the sharing pattern of data and its impact on Cache Coherence. Currently multicore platforms rely on hardware or automatic cache coherence techniques that allow programmers to develop programs without taking into account the problem. It is well known that standard coherency protocols are inefficient for certain data communication patterns and these inefficiencies will be amplified by the increased core number and the complex memory hierarchies. Following a structured parallelism approach, our methodology to attack these problems is based on two interrelated issues: structured parallelism paradigms and cost models (or performance models). Evaluating the performance of a program, although widely studied, is still an open problem in the research community and, notably, specific cost models to de- scribe multicores are missing. For this reason in this thesis, we define an abstract model for cache coherent architectures, which is able to capture the essential elements and the qualitative behaviors of multicore-based systems. Furthermore, we show how this abstract model combined with well known performance modelling techniques, such as analytical modelling (e.g., queueing models and stochastic process algebras) or simulations, provide an application- and architecture-dependent cost model to predict structured parallel applications performances. Starting out from the behavior and performance predictability of structured parallelism schemes, in this thesis we address the issue of cache coherence in multicore architectures, following an algorithm-dependent approach, a particular kind of software cache coherence solution characterized by explicit cache management strategies, which are specific of the algorithm to be executed. Notably, we ensure parallel correctness by exploiting architecture-specific mechanisms and by defining proper data structures in order to “emulate” cache coherence solutions in an efficient way for each computation. Algorithm-dependent cache coherence can be efficiently implemented at the support level of structured parallelism paradigms, with absolute transparency with respect to the application programmer. Moreover, by using the cost model, in this thesis we study and compare different algorithm-dependent implementations, such as those based on automatic cache coherence with respect to an original, non-automatic and lock-free solution based on interprocessor communications. Notably, with this latter implementation, in some cases, we are able to reduce the number of memory accesses, cache transfers and synchronizations and increasing computation parallelism with respect to the use of automatic cache coherence. Current architectures do not usually allow disabling automatic cache coherence. However, the emergence of many-core architectures somewhat changed the scenario, so that some architectures, such as the Tilera TilePro64, allow to control and disable the automatic cache coherence facilities. For this reason, in this thesis we finally apply our methodology to TilePro64 platform in order provide a further validation of the results obtained by our cost model

Improving Fine-Grained Irregular Shared-Memory Benchmarks by Data Reordering

We demonstrate that data reordering can substantially improve the performance of fine-grained irregular sharedmemory benchmarks, on both hardware and software shared-memory systems. In particular, we evaluate two distinct data reordering techniques that seek to co-locate in memory objects that are in close proximity in the physical system modeled by the computation. The effects of these techniques are increased spatial locality and reduced false sharing. We evaluate the effectiveness of the data reordering techniques on a set of five irregular applications from SPLASH-2 and Chaos. We implement both techniques in a small library, allowing us to enable them in an application by adding less than 10 lines of code. Our results on one hardware and two software shared-memory systems show that, with data reordering during initialization, the performance of these applications is improved by 12%–99% on the Origin 2000, 30%–366% on TreadMarks, and 14%–269% on HLRC

Directions in parallel programming: HPF, shared virtual memory and object parallelism in pC++

Fortran and C++ are the dominant programming languages used in scientific computation. Consequently, extensions to these languages are the most popular for programming massively parallel computers. We discuss two such approaches to parallel Fortran and one approach to C++. The High Performance Fortran Forum has designed HPF with the intent of supporting data parallelism on Fortran 90 applications. HPF works by asking the user to help the compiler distribute and align the data structures with the distributed memory modules in the system. Fortran-S takes a different approach in which the data distribution is managed by the operating system and the user provides annotations to indicate parallel control regions. In the case of C++, we look at pC++ which is based on a concurrent aggregate parallel model

Concurrent Probabilistic Simulation of High Temperature Composite Structural Response

A computational structural/material analysis and design tool which would meet industry's future demand for expedience and reduced cost is presented. This unique software 'GENOA' is dedicated to parallel and high speed analysis to perform probabilistic evaluation of high temperature composite response of aerospace systems. The development is based on detailed integration and modification of diverse fields of specialized analysis techniques and mathematical models to combine their latest innovative capabilities into a commercially viable software package. The technique is specifically designed to exploit the availability of processors to perform computationally intense probabilistic analysis assessing uncertainties in structural reliability analysis and composite micromechanics. The primary objectives which were achieved in performing the development were: (1) Utilization of the power of parallel processing and static/dynamic load balancing optimization to make the complex simulation of structure, material and processing of high temperature composite affordable; (2) Computational integration and synchronization of probabilistic mathematics, structural/material mechanics and parallel computing; (3) Implementation of an innovative multi-level domain decomposition technique to identify the inherent parallelism, and increasing convergence rates through high- and low-level processor assignment; (4) Creating the framework for Portable Paralleled architecture for the machine independent Multi Instruction Multi Data, (MIMD), Single Instruction Multi Data (SIMD), hybrid and distributed workstation type of computers; and (5) Market evaluation. The results of Phase-2 effort provides a good basis for continuation and warrants Phase-3 government, and industry partnership

Eureka: a distributed shared memory system based on the Lazy Data Merging consistency model

Distributed Shared Memory (DSM) provides an abstraction of shared memory on a network of workstations. Problems with existing DSM systems are lack of portability due to compiler and/or operating system modification requirements, and reduced performance due to significant synchronization and communication costs when compared to their message passing counterparts (e.g., PVM and MPI). Our approach was to introduce a new DSM consistency model, Lazy Data Merging (LDM), which extends Data Merging (DM). LDM is optimized for software runtime implementations and differs from DM by 'lazily' placing data updates across the communication network only when they are required. It is our belief that LDM can significantly reduce communication costs, particularly for applications that make extensive use of locks. We have completed the design of "Eureka", a prototype DSM system that provides a software implementation of the LDM consistency model. To ensure portability and efficiency we use only standard UniXTM system calls and a publicly available software thread package, Cthreads, from the University of Utah. Furthermore, we have implemented and tested some of Eureka's core components, specifically, the set of communication and hybrid (Invalidate/Update) coherence primitives, which are essential for follow on work in building the complete DSM system. The question of efficiency is still an open problem, because we did not compare Eureka with other DSM implementations.http://archive.org/details/eurekadistribute1094535209NANABrazilian Navy author