Dynamic resource allocation in a hierarchical multiprocessor system: A preliminary study

An integrated system approach to dynamic resource allocation is proposed. Some of the problems in dynamic resource allocation and the relationship of these problems to system structures are examined. A general dynamic resource allocation scheme is presented. A hierarchial system architecture which dynamically maps between processor structure and programs at multiple levels of instantiations is described. Simulation experiments were conducted to study dynamic resource allocation on the proposed system. Preliminary evaluation based on simple dynamic resource allocation algorithms indicates that with the proposed system approach, the complexity of dynamic resource management could be significantly reduced while achieving reasonable effective dynamic resource allocation

Structure driven multiprocessor compilation of numeric problems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1991.Title as it appears in the Feb. 1991 M.I.T. Graduate List: Structure driven compilation of numeric problems.Includes bibliographical references (leaves 134-136).by G.N. Srinivasa Prasanna.Ph.D

이종 멀티 코어 프로세서에서 SDF/L 그래프 스케줄링 기법

학위논문(석사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2021.8. Ha Soonhoi.Although dataflow models are known to thrive at exploiting task-level parallelism of an application, it is difficult to exploit the parallelism of data. Data-level parallelism can be represented well with loop structures, but these structures are not explicitly specified in most existing dataflow models. SDF/L model was introduced to overcome this shortcoming by specifying the loop structures explicitly in a hierarchical fashion. To the best of our knowledge however, scheduling of SDF/L graph onto heterogeneous processors has not been considered in any previous work. In this dissertation, we introduce a scheduling technique of an application represented by the SDF/L model onto heterogeneous processors. In the proposed method, we explore the mapping of tasks using an evolutionary meta-heuristic and schedule hierarchically in a bottom-up fashion, creating parallel loop schedules at lower levels first and then re-using them when constructing the schedule at a higher level. To verify the efficiency of the proposed scheduling methodology, we apply it to benchmark examples and randomly generated SDF/L graphs.데이터플로우 모델은 애플리케이션의 태스크를 병렬 처리할 때 좋은 모델로 알려져 있지만 데이터를 병렬로 처리하는 데에 활용하기는 어렵다. 데이터 수준 병렬 처리는 루프 구조를 통해 표현될 수 있으나 기존 데이터플로우 모델에서 명시적으로 루프 구조는 명세하는 방법이 없었다. 이러한 단점을 극복하기 위해 계층적 구조를 활용하여 루프 구조를 명시적으로 명세할 수 있는 SDF/L 모델이 제안되었다. 그러나 이기종 프로세서에 대한 SDF/L 그래프의 스케줄링은 이전까지 고려되지 않은 것으로 파악된다. 본 논문에서는 SDF/L 모델로 표현되는 애플리케이션을 이기종 프로세서에 대하여 스케줄링하는 기법을 소개한다. 제안된 방법에서는 먼저 진화적 메타 휴리스틱을 사용하여 태스크 매핑을 탐색한다. 이후 하위 수준에서 병렬 루프 스케줄을 만든 다음 상위 수준에서 스케줄 구성할 때 재사용하는 상향식의 계층적 태스크 스케줄링을 수행한다. 제안하는 스케줄링 기법의 효율성을 검증하기 위해 벤치마크 예제와 무작위로 생성된 SDF/L 그래프에 기법을 적용하였다.Chapter 1 Introduction 1 Chapter 2 Related Work 6 2.1 SDF Scheduling with Data-level Parallelism 8 2.2 Hierarchical Scheduling 9 Chapter 3 Problem and Challenges 11 3.1 Notations and Problem Description 11 3.2 Challenges 12 Chapter 4 Proposed methodology 15 4.1 Mapping Exploration 15 4.2 Priority Assignment and List Scheduling Heuristic 17 4.3 Hierarchical Scheduling 18 4.4 Complexity 23 Chapter 5 Experiments 24 5.1 Benchmarks 25 5.2 Randomly Generated Graphs 30 Chapter 6 Conclusions 35 Bibliography 37 요 약 41석

SCALABLE TECHNIQUES FOR SCHEDULING AND MAPPING DSP APPLICATIONS ONTO EMBEDDED MULTIPROCESSOR PLATFORMS

A variety of multiprocessor architectures has proliferated even for off-the-shelf computing platforms. To make use of these platforms, traditional implementation frameworks focus on implementing Digital Signal Processing (DSP) applications using special platform features to achieve high performance. However, due to the fast evolution of the underlying architectures, solution redevelopment is error prone and re-usability of existing solutions and libraries is limited. In this thesis, we facilitate an efficient migration of DSP systems to multiprocessor platforms while systematically leveraging previous investment in optimized library kernels using dataflow design frameworks. We make these library elements, which are typically tailored to specialized architectures, more amenable to extensive analysis and optimization using an efficient and systematic process. In this thesis we provide techniques to allow such migration through four basic contributions: 1. We propose and develop a framework to explore efficient utilization of Single Instruction Multiple Data (SIMD) cores and accelerators available in heterogeneous multiprocessor platforms consisting of General Purpose Processors (GPPs) and Graphics Processing Units (GPUs). We also propose new scheduling techniques by applying extensive block processing in conjunction with appropriate task mapping and task ordering methods that match efficiently with the underlying architecture. The approach gives the developer the ability to prototype a GPU-accelerated application and explore its design space efficiently and effectively. 2. We introduce the concept of Partial Expansion Graphs (PEGs) as an implementation model and associated class of scheduling strategies. PEGs are designed to help realize DSP systems in terms of forms and granularities of parallelism that are well matched to the given applications and targeted platforms. PEGs also facilitate derivation of both static and dynamic scheduling techniques, depending on the amount of variability in task execution times and other operating conditions. We show how to implement efficient PEG-based scheduling methods using real time operating systems, and to re-use pre-optimized libraries of DSP components within such implementations. 3. We develop new algorithms for scheduling and mapping systems implemented using PEGs. Collectively, these algorithms operate in three steps. First, the amount of data parallelism in the application graph is tuned systematically over many iterations to profit from the available cores in the target platform. Then a mapping algorithm that uses graph analysis is developed to distribute data and task parallel instances over different cores while trying to balance the load of all processing units to make use of pipeline parallelism. Finally, we use a novel technique for performance evaluation by implementing the scheduler and a customizable solution on the programmable platform. This allows accurate fitness functions to be measured and used to drive runtime adaptation of schedules. 4. In addition to providing scheduling techniques for the mentioned applications and platforms, we also show how to integrate the resulting solution in the underlying environment. This is achieved by leveraging existing libraries and applying the GPP-GPU scheduling framework to augment a popular existing Software Defined Radio (SDR) development environment -- GNU Radio -- with a dataflow foundation and a stand-alone GPU-accelerated library. We also show how to realize the PEG model on real time operating system libraries, such as the Texas Instruments DSP/BIOS. A code generator that accepts a manual system designer solution as well as automatically configured solutions is provided to complete the design flow starting from application model to running system

Dataflow development of medium-grained parallel software

PhD ThesisIn the 1980s, multiple-processor computers (multiprocessors) based on conven- tional processing elements emerged as a popular solution to the continuing demand for ever-greater computing power. These machines offer a general-purpose parallel processing platform on which the size of program units which can be efficiently executed in parallel - the "grain size" - is smaller than that offered by distributed computing environments, though greater than that of some more specialised architectures. However, programming to exploit this medium-grained parallelism remains difficult. Concurrent execution is inherently complex, yet there is a lack of programming tools to support parallel programming activities such as program design, implementation, debugging, performance tuning and so on. In helping to manage complexity in sequential programming, visual tools have often been used to great effect, which suggests one approach towards the goal of making parallel programming less difficult. This thesis examines the possibilities which the dataflow paradigm has to offer as the basis for a set of visual parallel programming tools, and presents a dataflow notation designed as a framework for medium-grained parallel programming. The implementation of this notation as a programming language is discussed, and its suitability for the medium-grained level is examinedScience and Engineering Research Council of Great Britain EC ERASMUS schem

Integrating compile-time and runtime parallelism management through revocable thread serialization

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (leaves 125-128).by Gino K. Maa.Ph.D

Energy-Aware Data Management on NUMA Architectures

The ever-increasing need for more computing and data processing power demands for a continuous and rapid growth of power-hungry data center capacities all over the world. As a first study in 2008 revealed, energy consumption of such data centers is becoming a critical problem, since their power consumption is about to double every 5 years. However, a recently (2016) released follow-up study points out that this threatening trend was dramatically throttled within the past years, due to the increased energy efficiency actions taken by data center operators. Furthermore, the authors of the study emphasize that making and keeping data centers energy-efficient is a continuous task, because more and more computing power is demanded from the same or an even lower energy budget, and that this threatening energy consumption trend will resume as soon as energy efficiency research efforts and its market adoption are reduced. An important class of applications running in data centers are data management systems, which are a fundamental component of nearly every application stack. While those systems were traditionally designed as disk-based databases that are optimized for keeping disk accesses as low a possible, modern state-of-the-art database systems are main memory-centric and store the entire data pool in the main memory, which replaces the disk as main bottleneck. To scale up such in-memory database systems, non-uniform memory access (NUMA) hardware architectures are employed that face a decreased bandwidth and an increased latency when accessing remote memory compared to the local memory. In this thesis, we investigate energy awareness aspects of large scale-up NUMA systems in the context of in-memory data management systems. To do so, we pick up the idea of a fine-grained data-oriented architecture and improve the concept in a way that it keeps pace with increased absolute performance numbers of a pure in-memory DBMS and scales up on NUMA systems in the large scale. To achieve this goal, we design and build ERIS, the first scale-up in-memory data management system that is designed from scratch to implement a data-oriented architecture. With the help of the ERIS platform, we explore our novel core concept for energy awareness, which is Energy Awareness by Adaptivity. The concept describes that software and especially database systems have to quickly respond to environmental changes (i.e., workload changes) by adapting themselves to enter a state of low energy consumption. We present the hierarchically organized Energy-Control Loop (ECL), which is a reactive control loop and provides two concrete implementations of our Energy Awareness by Adaptivity concept, namely the hardware-centric Resource Adaptivity and the software-centric Storage Adaptivity. Finally, we will give an exhaustive evaluation regarding the scalability of ERIS as well as our adaptivity facilities

Visual object-oriented development of parallel applications

PhD ThesisDeveloping software for parallel architectures is a notoriously difficult task, compounded further by the range of available parallel architectures. There has been little research effort invested in how to engineer parallel applications for more general problem domains than the traditional numerically intensive domain. This thesis addresses these issues. An object-oriented paradigm for the development of general-purpose parallel applications, with full lifecycle support, is proposed and investigated, and a visual programming language to support that paradigm is developed. This thesis presents experiences and results from experiments with this new model for parallel application development.Engineering and Physical Sciences Research Council

The exploitation of parallelism on shared memory multiprocessors

PhD ThesisWith the arrival of many general purpose shared memory multiple processor (multiprocessor) computers into the commercial arena during the mid-1980's, a rift has opened between the raw processing power offered by the emerging hardware and the relative inability of its operating software to effectively deliver this power to potential users. This rift stems from the fact that, currently, no computational model with the capability to elegantly express parallel activity is mature enough to be universally accepted, and used as the basis for programming languages to exploit the parallelism that multiprocessors offer. To add to this, there is a lack of software tools to assist programmers in the processes of designing and debugging parallel programs. Although much research has been done in the field of programming languages, no undisputed candidate for the most appropriate language for programming shared memory multiprocessors has yet been found. This thesis examines why this state of affairs has arisen and proposes programming language constructs, together with a programming methodology and environment, to close the ever widening hardware to software gap. The novel programming constructs described in this thesis are intended for use in imperative languages even though they make use of the synchronisation inherent in the dataflow model by using the semantics of single assignment when operating on shared data, so giving rise to the term shared values. As there are several distinct parallel programming paradigms, matching flavours of shared value are developed to permit the concise expression of these paradigms.The Science and Engineering Research Council