The distributed ASCI supercomputer project

The Distributed ASCI Supercomputer (DAS) is a homogeneous wide-area distributed system consisting of four cluster computers at different locations. DAS has been used for research on communication software, parallel languages and programming systems, schedulers, parallel applications, and distributed applications. The paper gives a preview of the most interesting research results obtained so far in the DAS project

A Comprehensive Survey on Distributed Training of Graph Neural Networks

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training which distributes the workload of training across multiple computing nodes. At present, the volume of related research on distributed GNN training is exceptionally vast, accompanied by an extraordinarily rapid pace of publication. Moreover, the approaches reported in these studies exhibit significant divergence. This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training. As a result, there is a pressing need for a survey to provide correct recognition, analysis, and comparisons in this field. In this paper, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks, emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.Comment: To Appear in Proceedings of the IEE

Side-channel Attacks with Multi-thread Mixed Leakage

Side-channel attacks are one of the greatest practical threats to security-related applications, because they are capable of breaking ciphers that are assumed to be mathematically secure. Lots of studies have been devoted to power or electro-magnetic (EM) analysis against desktop CPUs, mobile CPUs (including ARM, MSP, AVR, etc) and FPGAs, but rarely targeted modern GPUs. Modern GPUs feature their special and specific single instruction multiple threads (SIMT) execution fashion, which makes their power/EM leakage more sophisticated in practical scenarios. In this paper, we study side-channel attacks with leakage from SIMT systems, and propose leakage models suited to any SIMT systems and specifically to CUDA-enabled GPUs. Afterwards, we instantiate the models with a GPU AES implementation, which is also used for performance evaluations. In addition to the models, we provide optimizations on the attacks that are based on the models. To evaluate the models and optimizations, we run the GPU AES implementation on a CUDA-enabled GPU and, at the same time, collect its EM leakage. The experimental results show that the proposed models are more efficient and the optimizations are effective as well. Our study suggests that GPU-based cryptographic implementations may be much vulnerable to microarchitecture-based side-channel attacks. Therefore, GPU-specific countermeasures should be considered for GPU-based cryptographic implementations in practical applications

Vectorization system for unstructured codes with a Data-parallel Compiler IR

With Dennard Scaling coming to an end, Single Instruction Multiple Data (SIMD) offers itself as a way to improve the compute throughput of CPUs. One fundamental technique in SIMD code generators is the vectorization of data-parallel code regions. This has applications in outer-loop vectorization, whole-function vectorization and vectorization of explicitly data-parallel languages. This thesis makes contributions to the reliable vectorization of data-parallel code regions with unstructured, reducible control flow. Reducibility is the case in practice where all control-flow loops have exactly one entry point. We present P-LLVM, a novel, full-featured, intermediate representation for vectorizers that provides a semantics for the code region at every stage of the vectorization pipeline. Partial control-flow linearization is a novel partial if-conversion scheme, an essential technique to vectorize divergent control flow. Different to prior techniques, partial linearization has linear running time, does not insert additional branches or blocks and gives proved guarantees on the control flow retained. Divergence of control induces value divergence at join points in the control-flow graph (CFG). We present a novel control-divergence analysis for directed acyclic graphs with optimal running time and prove that it is correct and precise under common static assumptions. We extend this technique to obtain a quadratic-time, control-divergence analysis for arbitrary reducible CFGs. For this analysis, we show on a range of realistic examples how earlier approaches are either less precise or incorrect. We present a feature-complete divergence analysis for P-LLVM programs. The analysis is the first to analyze stack-allocated objects in an unstructured control setting. Finally, we generalize single-dimensional vectorization of outer loops to multi-dimensional tensorization of loop nests. SIMD targets benefit from tensorization through more opportunities for re-use of loaded values and more efficient memory access behavior. The techniques were implemented in the Region Vectorizer (RV) for vectorization and TensorRV for loop-nest tensorization. Our evaluation validates that the general-purpose RV vectorization system matches the performance of more specialized approaches. RV performs on par with the ISPC compiler, which only supports its structured domain-specific language, on a range of tree traversal codes with complex control flow. RV is able to outperform the loop vectorizers of state-of-the-art compilers, as we show for the SPEC2017 nab_s benchmark and the XSBench proxy application.Mit dem Ausreizen des Dennard Scalings erreichen die gewohnten Zuwächse in der skalaren Rechenleistung zusehends ihr Ende. Moderne Prozessoren setzen verstärkt auf parallele Berechnung, um den Rechendurchsatz zu erhöhen. Hierbei spielen SIMD Instruktionen (Single Instruction Multiple Data), die eine Operation gleichzeitig auf mehrere Eingaben anwenden, eine zentrale Rolle. Eine fundamentale Technik, um SIMD Programmcode zu erzeugen, ist der Einsatz datenparalleler Vektorisierung. Diese unterliegt populären Verfahren, wie der Vektorisierung äußerer Schleifen, der Vektorisierung gesamter Funktionen bis hin zu explizit datenparallelen Programmiersprachen. Der Beitrag der vorliegenden Arbeit besteht darin, ein zuverlässiges Vektorisierungssystem für datenparallelen Code mit reduziblem Steuerfluss zu entwickeln. Diese Anforderung ist für alle Steuerflussgraphen erfüllt, deren Schleifen nur einen Eingang haben, was in der Praxis der Fall ist. Wir präsentieren P-LLVM, eine ausdrucksstarke Zwischendarstellung für Vektorisierer, welche dem Programm in jedem Stadium der Transformation von datenparallelem Code zu SIMD Code eine definierte Semantik verleiht. Partielle Steuerfluss-Linearisierung ist ein neuer Algorithmus zur If-Conversion, welcher Sprünge erhalten kann. Anders als existierende Verfahren hat Partielle Linearisierung eine lineare Laufzeit und fügt keine neuen Sprünge oder Blöcke ein. Wir zeigen Kriterien, unter denen der Algorithmus Steuerfluss erhält, und beweisen diese. Steuerflussdivergenz induziert Divergenz an Punkten zusammenfließenden Steuerflusses. Wir stellen eine neue Steuerflussdivergenzanalyse für azyklische Graphen mit optimaler Laufzeit vor und beweisen deren Korrektheit und Präzision. Wir verallgemeinern die Technik zu einem Algorithmus mit quadratischer Laufzeit für beliebiege, reduzible Steuerflussgraphen. Eine Studie auf realistischen Beispielgraphen zeigt, dass vergleichbare Techniken entweder weniger präsize sind oder falsche Ergebnisse liefern. Ebenfalls präsentieren wir eine Divergenzanalyse für P-LLVM Programme. Diese Analyse ist die erste Divergenzanalyse, welche Divergenz in stapelallokierten Objekten unter unstrukturiertem Steuerfluss analysiert. Schließlich generalisieren wir die eindimensionale Vektorisierung von äußeren Schleifen zur multidimensionalen Tensorisierung von Schleifennestern. Tensorisierung eröffnet für SIMD Prozessoren mehr Möglichkeiten, bereits geladene Werte wiederzuverwenden und das Speicherzugriffsverhalten des Programms zu optimieren, als dies mit Vektorisierung der Fall ist. Die vorgestellten Techniken wurden in den Region Vectorizer (RV) für Vektorisierung und TensorRV für die Tensorisierung von Schleifennestern implementiert. Wir zeigen auf einer Reihe von steuerflusslastigen Programmen für die Traversierung von Baumdatenstrukturen, dass RV das gleiche Niveau erreicht wie der ISPC Compiler, welcher nur seine strukturierte Eingabesprache verarbeiten kann. RV kann schnellere SIMD-Programme erzeugen als die Schleifenvektorisierer in aktuellen Industriecompilern. Dies demonstrieren wir mit dem nab_s benchmark aus der SPEC2017 Benchmarksuite und der XSBench Proxy-Anwendung

The relationship between search based software engineering and predictive modeling

Search Based Software Engineering (SBSE) is an approach to software engineering in which search based optimization algorithms are used to identify optimal or near optimal solutions and to yield insight. SBSE techniques can cater for multiple, possibly competing objectives and/or constraints and applications where the potential solution space is large and complex. This paper will provide a brief overview of SBSE, explaining some of the ways in which it has already been applied to construction of predictive models. There is a mutually beneficial relationship between predictive models and SBSE. The paper sets out eleven open problem areas for Search Based Predictive Modeling and describes how predictive models also have role to play in improving SBSE

서비스 균등 분배와 고성능을 위한 다중프로세서칩 상의 재구성형 통신 구조

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 최기영.The chip multiprocessor (CMP) era has long begun due to the diminishing return from instruction-level parallelism (ILP) harvesting techniques, the rising power and temperature from frequency scaling, etc. One powerful processor has been replaced by many less-powerful processors forming a CMP. One of the issues arose from this paradigm shift is the management of communication among the processors. Buses, which has been a common choice for the systems with one or several processors, failed to sustain the increased communication burden of CMPs. Many bus-based improvements including hierarchical buses and bus-matrices, were proposed but eventually, network-on-chip (NoC) has become the de facto standard for designing a CMP system, replacing the bus-based techniques. NoCs strengths over bus mainly come from its capability of conveying multiple transactions simultaneously from different components to the others. The concurrent communications between the cores are conducted by the distributed, yet shared network components, routers. Routers provide cores with services such as bandwidths. One of the design issues in implementing NoC is to distribute these services evenly across all the cores requesting for them. Arbiter is a component that regulates the accesses to shared resources such as channels and buffers. It has the policy under which requests get services in turn from the shared resources so that the requestors dont fall into deadlock or starvation. One of the common policies for an arbiter is the round-robin, where requests get their grant one by one so that fairness is assured among the requestors. When applied to routers in NoC, it fails to provide the fairness because each request goes through multiple routers, thus multiple round-robin arbiters on a transaction route. The cascaded effect of the round-robin arbitration is that the farther a source is from the destination, the less service it gets from the destination. The first part of this thesis addresses this issue, and proposes thus far the simplest yet the most effective way of providing the fairness to all the nodes on NoC. It applies weighted round-robin scheme where the weights are determined at run-time depending on which cores are allocated to applications or threads running on the CMP. RTL implementation and synthesis are done to show the simplicity of the proposed scheme. Simulation with synthetic traffic patterns and SPEC CPU2006 benchmark applications show that the proposed approach results in outstanding equality-of-service characteristics. The second part of this thesis deals with the impact of the reconfigurable communication architecture on the performance of a CMP system. One of the pitfalls of NoC is long access latency due to increased hop count between a source and its destination. For example, NoC with mesh topology has its hop count proportional to its size. Because of this, while being a common choice for CMP, mesh topology is said to be inscalable in terms of the number of cores. Some alternatives to mesh topology exist, one of them being high radix NoCs. They replace short and wide channels of mesh with long and narrow ones achieving fewer hop counts. Another option is to cluster cores so that the dimension of mesh network reduces. The clusters are formed by grouping cores via local communication fabric. The clusters are interconnected by a global communication fabric, often in the shape of mesh topology. Many types of local communication fabric are explored in previous researches, including another NoC with topologies of mesh, ring, etc. However, bus has become one of the most favorable choices for the local connection because of its simplicity. The simplicity leads local communications to be performed with high performance, low chip area, low power consumption, etc. One of the issues in forming core clusters in CMP is their grain size. Tying too many cores into a cluster results in the congestion on the bus, reducing the performance of the local communications. On the other hand, too few cores in a cluster misses the chances of improving system performance by efficient local communications through the bus. It is obvious that the optimal number of cores in a cluster depends on the applications that run on the CMP. Bus reconfiguration with bus segments and switches can be a solution for varying cluster size on a CMP. In addition to the variable cluster sizes, bus reconfiguration has another advantage of processor (not process) migration. Bus reconfiguration can reconnect cores and caches so that the distance between cores and data are reduced dynamically. In this way, data copies and network transactions can be dramatically reduced to improve the system performance. The second part of this thesis addresses this issue and proposes a reconfigurable bus-mesh architecture to accelerate pipelined applications. With the proposed architecture, the data transfer between the successive pipeline stages are done not by data copies but by processor migrations. Systematic management of bus segments and L1 data caches are required to achieve efficient use of the reconfigurability. The proposed architecture is compared with the baseline architecture, which maintains cache coherence with hardware. Multilayer perceptron (MLP), convolutional neural network (CNN), and JPEG decoder are implemented as example pipelined applications using multi-threaded programming model. The in-house full system simulator is implemented and used to measure the performance improvement of the proposed architecture. The experimental results show that 21.75 %, 14.40 %, and 12.74 % execution cycle reductions are achieved for MLP, CNN, and JPEG decoder, respectively.Part I Adaptively Weighted Round-Robin Arbitration for Equality of Service in a Many-Core Network-on-Chip [1] 1 Chapter 1 Introduction 3 Chapter 2 Previous Work 7 Chapter 3 Position-Based Weighted Round-Robin Arbitration 11 Chapter 4 Adaptively Weighted Round-Robin Arbitration 17 4.1 Hardware Implementation for weight update 18 4.2 Arbitration Weight Determination 22 Chapter 5 Experimental Results 25 5.1 Open-Loop Measurements 25 5.2 Closed-Loop Measurements 29 5.3 Hardware Implementation 33 Chapter 6 Conclusion 35 Part II Accelerating Pipelined Applications with Reconfigurable Bus-Mesh Communication Architecture in Chip Multiprocessors 37 Chapter 7 Introduction 39 Chapter 8 Backgrounds and Previous Work 43 8.1 Segmented Bus 43 8.2 CMPs with Reconfigurable Bus-Mesh Communication Architecture 44 8.3 Near-Threshold Computing 48 Chapter 9 Baseline Architecture 51 Chapter 10 Motivation 55 Chapter 11 Reconfigurable Bus-Mesh Architecture 61 11.1 Thread Programming Model 61 11.2 Cluster Size 64 11.3 Organizing Multiple L1Ds and SPM Banks in a Cluster 66 11.4 L1 Data Cache / SPM Partitioning 70 11.5 Reconfiguration Overheads 71 Chapter 12 Experimental Results 75 12.1 Pipelined Applications 75 12.2 Simulation Environment 78 12.3 Memory Operations Latency Breakdown 79 Chapter 13 Conclusion 85 Bibliography 87 국문초록 95Docto

Advanced modelling and visualisation of liquid-liquid separations of complex sample components, with variable phase distribution and mode of operation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research is about liquid-liquid chromatography modelling. While the main focus was on liquid-liquid chromatography, where the stationary and mobile phases are both liquid, theory of different types of chromatography, including the currently most used techniques, were considered as well. The main goal of this research was to develop a versatile liquid-liquid separation model, able to model all potential operating scenarios and modes of operation. A second goal was to create effective and usable interfaces to such a model, implying primarily information visualisation, and secondarily educative visualisation. The first model developed was a model based on Counter-Current Distribution. Next a new more elemental model was developed, the probabilistic model, which better models continuous liquid-liquid chromatography techniques. Finally, a more traditional model was developed using transport theory. These models were used and compared to experimental data taken from literature. The models were demonstrated to model all main liquid-liquid chromatography techniques, incorporated the different modes of operation, and were able to accurately model many sample components and complex sample injections. A model interface was developed, permitting functional and effective model configuration, exploration and analysis using visualisation and interactivity. Different versions of the interface were then evaluated using questionnaires, group interviews and Insight Evaluation. The visualisation and interactivity enhancements have proven to contribute understanding and insight of the underlying chromatography process. This also proved the value of the Insight Evaluation method, providing valuable qualitative evaluation results desired for this model interface evaluation. A prototype of a new graphical user interface developed, and showed great potential for combining model parameter input and exploring the liquid-liquid chromatography processes. Additionally, a new visualisation method was developed that can accurately visualise different modes of operation. This was used to create animations, which were also evaluated. The results of this evaluation show the new visualisation helps understanding of the liquid-liquid chromatography process amongst CCC novices. The model software will be a valuable tool for industry for predicting, evaluating and validating experimental separations and production processes. While effective models already existed, the use of interactive visualisation permits users to explore the relationship between parameters and performances in a simpler yet more powerful way. It will also be a valuable tool for academia for teaching & training, both staff and students, on how to use the technology. Prior to this work no such tool existed or existing tools were limited in their accessibility and educational value.This study was supported by Brunel University and the Royal Academy of Engineering

Clustered VLIW architecture based on queue register files

Institute for Computing Systems ArchitectureInstruction-level parallelism (ILP) is a set of hardware and software techniques that allow parallel execution of machine operations. Superscalar architectures rely most heavily upon hardware schemes to identify parallelism among operations. Although successful in terms of performance, the hardware complexity involved might limit the scalability of this model. VLIW architectures use a different approach to exploit ILP. In this case all data dependence analyses and scheduling of operations are performed at compile time, resulting in a simpler hardware organization. This allows the inclusion of a larger number of functional units (FUs) into a single chip. IN spite of this relative simplification, the scalability of VLIW architectures can be constrained by the size and number of ports of the register file. VLIW machines often use software pipelining techniques to improve the execution of loop structures, which can increase the register pressure. Furthermore, the access time of a register file can be compromised by the number of ports, causing a negative impact on the machine cycle time. For these reasons we understand that the benefits of having parallel FUs, which have motivated the investigation of alternative machine designs. This thesis presents a scalar VLIW architecture comprising clusters of FUs and private register files. Register files organised as queue structures are used as a mechanism for inter-cluster communication, allowing the enforcement of fixed latency in the process. This scheme presents better possibilities in terms of scalability as the size of the individual register files is not determined by the total number of FUs, suggesting that the silicon area may grow only linearly with respect to the total number of FUs. However, the effectiveness of such an organization depends on the efficiency of the code partitioning strategy. We have developed an algorithm for a clustered VLIW architecture integrating both software pipelining and code partitioning in a a single procedure. Experimental results show it may allow performance levels close to an unclustered machine without communication restraints. Finally, we have developed silicon area and cycle time models to quantify the scalability of performance and cost for this class of architecture