Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Data Mining - Concepts and Techniques.

On-chip micronetworks, designed with a layered methodology, will meet the distinctive challenges of providing functionally correct, reliable operation of interacting system-on-chip components. A system on chip (SoC) can provide an integrated solution to challenging design problems in the telecommunications, multimedia, and consumer electronics domains. Much of the progress in these fields hinges on the designers' ability to conceive complex electronic engines under strong time-to-market pressure. Success will require using appropriate design and process technologies, as well as interconnecting existing components reliably in a plug-and-play fashion. Focusing on using probabilistic metrics such as average values or variance to quantify design objectives such as performance and power will lead to a major change in SoC design methodologies. Overall, these designs will be based on both deterministic and stochastic models. Creating complex SoCs requires a modular, component-based approach to both hardware and software design. Despite numerous challenges, the authors believe that developers will solve the problems of designing SoC networks. At the same time, they believe that a layered micronetwork design methodology will likely be the only path to mastering the complexity of future SoC designs.

Networks on chips: a new SoC paradigm

One of the greatest challenges faced by designers of digital systems is optimizing the communication and interconnection between system components. Interconnection networks offer an attractive and economical solution to this communication crisis and are fast becoming pervasive in digital systems. Current trends suggest that this communication bottleneck will be even more problematic when designing future generations of machines. Consequently, the anatomy of an interconnection network router and science of interconnection network design will only grow in importance in the coming years.

This book offers a detailed and comprehensive presentation of the basic principles of interconnection network design, clearly illustrating them with numerous examples, chapter exercises, and case studies. It incorporates hardware-level descriptions of concepts, allowing a designer to see all the steps of the process from abstract design to concrete implementation.

·Case studies throughout the book draw on extensive author experience in designing interconnection networks over a period of more than twenty years, providing real world examples of what works, and what doesn't.

·Tightly couples concepts with implementation costs to facilitate a deeper understanding of the tradeoffs in the design of a practical network.

·A set of examples and exercises in every chapter help the reader to fully understand all the implications of every design decision.

Table of Contents


Chapter 1 Introduction to Interconnection Networks 
1.1 Three Questions About Interconnection Networks 
1.2 Uses of Interconnection Networks 
1.3 Network Basics 
1.4 History 
1.5 Organization of this Book 

Chapter 2 A Simple Interconnection Network 
2.1 Network Specifications and Constraints 
2.2 Topology 
2.3 Routing 
2.4 Flow Control 
2.5 Router Design 
2.6 Performance Analysis 
2.7 Exercises 

Chapter 3 Topology Basics 
3.1 Nomenclature 
3.2 Traffic Patterns 
3.3 Performance 
3.4 Packaging Cost 
3.5 Case Study: The SGI Origin 2000 
3.6 Bibliographic Notes 
3.7 Exercises 

Chapter 4 Butterfly Networks 
4.1 The Structure of Butterfly Networks 
4.2 Isomorphic Butterflies 
4.3 Performance and Packaging Cost 
4.4 Path Diversity and Extra Stages 
4.5 Case Study: The BBN Butterfly 
4.6 Bibliographic Notes 
4.7 Exercises 

Chapter 5 Torus Networks 
5.1 The Structure of Torus Networks 
5.2 Performance 
5.3 Building Mesh and Torus Networks 
5.4 Express Cubes 
5.5 Case Study: The MIT J-Machine 
5.6 Bibliographic Notes 
5.7 Exercises 
Chapter 6 Non-Blocking Networks 
6.1 Non-Blocking vs. Non-Interfering Networks 
6.2 Crossbar Networks 
6.3 Clos Networks 
6.4 Benes Networks 
6.5 Sorting Networks 
6.6 Case Study: The Velio VC2002 (Zeus) Grooming Switch 
6.7 Bibliographic Notes 
6.8 Exercises 

Chapter 7 Slicing and Dicing 
7.1 Concentrators and Distributors 
7.2 Slicing and Dicing 
7.3 Slicing Multistage Networks 
7.4 Case Study: Bit Slicing in the Tiny Tera 
7.5 Bibliographic Notes 
7.6 Exercises 

Chapter 8 Routing Basics 
8.1 A Routing Example 
8.2 Taxonomy of Routing Algorithms 
8.3 The Routing Relation 
8.4 Deterministic Routing 
8.5 Case Study: Dimension-Order Routing in the Cray T3D 
8.6 Bibliographic Notes 
8.7 Exercises 

Chapter 9 Oblivious Routing 
9.1 Valiant's Randomized Routing Algorithm 
9.2 Minimal Oblivious Routing 
9.3 Load-Balanced Oblivious Routing 
9.4 Analysis of Oblivious Routing 
9.5 Case Study: Oblivious Routing in the
Avici Terabit Switch Router(TSR) 
9.6 Bibliographic Notes 
9.7 Exercises 

Chapter 10 Adaptive Routing 
10.1 Adaptive Routing Basics 
10.2 Minimal Adaptive Routing 
10.3 Fully Adaptive Routing 
10.4 Load-Balanced Adaptive Routing 
10.5 Search-Based Routing 
10.6 Case Study: Adaptive Routing in the
Thinking Machines CM-5 
10.7 Bibliographic Notes 
10.8 Exercises 

Chapter 11 Routing Mechanics 
11.1 Table-Based Routing 
11.2 Algorithmic Routing 
11.3 Case Study: Oblivious Source Routing in the
IBM Vulcan Network 
11.4 Bibliographic Notes 
11.5 Exercises 

Chapter 12 Flow Control Basics 
12.1 Resources and Allocation Units 
12.2 Bufferless Flow Control 
12.3 Circuit Switching 
12.4 Bibliographic Notes 
12.5 Exercises 

Chapter 13 Buffered Flow Control 
13.1 Packet-Buffer Flow Control 
13.2 Flit-Buffer Flow Control 
13.3 Buffer Management and Backpressure 
13.4 Flit-Reservation Flow Control 
13.5 Bibliographic Notes 
13.6 Exercises 

Chapter 14 Deadlock and Livelock 
14.1 Deadlock 
14.2 Deadlock Avoidance 
14.3 Adaptive Routing 
14.4 Deadlock Recovery 
14.5 Livelock 
14.6 Case Study: Deadlock Avoidance in the Cray T3E 
14.7 Bibliographic Notes 
14.8 Exercises 

Chapter 15 Quality of Service 
15.1 Service Classes and Service Contracts 
15.2 Burstiness and Network Delays 
15.3 Implementation of Guaranteed Services 
15.4 Implementation of Best-Effort Services 
15.5 Separation of Resources 
15.6 Case Study: ATM Service Classes 
15.7 Case Study: Virtual Networks in the Avici TSR 
15.8 Bibliographic Notes 
15.9 Exercises 

Chapter 16 Router Architecture 
16.1 Basic Router Architecture 
16.2 Stalls 
16.3 Closing the Loop with Credits 
16.4 Reallocating a Channel 
16.5 Speculation and Lookahead 
16.6 Flit and Credit Encoding 
16.7 Case Study: The Alpha 21364 Router 
16.8 Bibliographic Notes 
16.9 Exercises 

Chapter 17 Router Datapath Components 
17.1 Input Buffer Organization 
17.2 Switches 
17.3 Output Organization 
17.4 Case Study: The Datapath of the IBM Colony
Router 
17.5 Bibliographic Notes 
17.6 Exercises 

Chapter 18 Arbitration 
18.1 Arbitration Timing 
18.2 Fairness 
18.3 Fixed Priority Arbiter 
18.4 Variable Priority Iterative Arbiters 
18.5 Matrix Arbiter 
18.6 Queuing Arbiter 
18.7 Exercises 

Chapter 19 Allocation 
19.1 Representations
19.2 Exact Algorithms
19.3 Separable Allocators 
19.4 Wavefront Allocator 
19.5 Incremental vs. Batch Allocation 
19.6 Multistage Allocation 
19.7 Performance of Allocators 
19.8 Case Study: The Tiny Tera Allocator 
19.9 Bibliographic Notes 
19.10 Exercises

Chapter 20 Network Interfaces 
20.1 Processor-Network Interface 
20.2 Shared-Memory Interface 
20.3 Line-Fabric Interface 
20.4 Case Study: The MIT M-Machine Network Interface 
20.5 Bibliographic Notes 
20.6 Exercises 

Chapter 21 Error Control 411
21.1 Know Thy Enemy: Failure Modes and Fault Models 
21.2 The Error Control Process: Detection, Containment,
and Recovery 
21.3 Link Level Error Control 
21.4 Router Error Control 
21.5 Network-Level Error Control 
21.6 End-to-end Error Control 
21.7 Bibliographic Notes 
21.8 Exercises 

Chapter 22 Buses 
22.1 Bus Basics 
22.2 Bus Arbitration 
22.3 High Performance Bus Protocol 
22.4 From Buses to Networks 
22.5 Case Study: The PCI Bus 
22.6 Bibliographic Notes 
22.7 Exercises 

Chapter 23 Performance Analysis 
23.1 Measures of Interconnection Network Performance 
23.2 Analysis 
23.3 Validation
23.4 Case Study: Efficiency and Loss in the
BBN Monarch Network 
23.5 Bibliographic Notes 
23.6 Exercises 

Chapter 24 Simulation 
24.1 Levels of Detail 
24.2 Network Workloads 
24.3 Simulation Measurements 
24.4 Simulator Design 
24.5 Bibliographic Notes 
24.6 Exercises 

Chapter 25 Simulation Examples 495
25.1 Routing
25.2 Flow Control Performance 
25.3 Fault Tolerance 

Appendix A Nomenclature 
Appendix B Glossary 
Appendix C Network Simulator

Principles and Practices of Interconnection Networks

Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.

In-Datacenter Performance Analysis of a Tensor Processing Unit

From the Publisher:
Addresses the challenges and details the basic underlying concepts of interconnection networks. The book's engineering approach considers the issues that designers need to deal with and presents a broad set of practical solutions. Considerable effort is made to establish new and more.

Interconnection Networks: An Engineering Approach

This paper presents a programmable and scalable digital neuromorphic architecture based on 3D high-density memory integrated with logic tier for efficient neural computing. The proposed architecture consists of clusters of processing engines, connected by 2D mesh network as a processing tier, which is integrated in 3D with multiple tiers of DRAM. The PE clusters access multiple memory channels (vaults) in parallel. The operating principle, referred to as the memory centric computing, embeds specialized state-machines within the vault controllers of HMC to drive data into the PE clusters. The paper presents the basic architecture of the Neurocube and an analysis of the logic tier synthesized in 28nm and 15nm process technologies. The performance of the Neurocube is evaluated and illustrated through the mapping of a Convolutional Neural Network and estimating the subsequent power and performance for both training and inference.

Neurocube: a programmable digital neuromorphic architecture with high-density 3D memory

The performance of most digital systems today is limited by their communication or interconnection, not by their logic or memory. As designers strive to make more efficient use of scarce interconnection bandwidth, interconnection networks are emerging as a nearly universal solution to the system-level communication problems for modern digital systems. 


Interconnection networks have become pervasive in their traditional application as processor-memory and processor-processor interconnect. Point-to-point interconnection networks have replaced buses in an ever widening range of applications that include on-chip interconnect, switches and routers, and I/O systems. 


In this book, the authors present in a structured way the basic underlying concepts of most interconnection networks and provide representative solutions that have been implemented in the industry or proposed in the research literature.

* Gives a coherent, comprehensive treatment of the entire field
* Presents a formal statement of the basic concepts, alternative design choices, and design trade-offs
* Provides thorough classifications, clear descriptions, accurate definitions, and unified views to structure the knowledge on interconnection networks
* Focuses on issues critical to designers

Table of Contents

Foreword
Foreword to the First Printing
Preface
Chapter 1 - Introduction
Chapter 2 - Message Switching Layer
Chapter 3 - Deadlock, Livelock, and Starvation
Chapter 4 - Routing Algorithms
Chapter 5 - CollectiveCommunicationSupport
Chapter 6 - Fault-Tolerant Routing
Chapter 7 - Network Architectures
Chapter 8 - Messaging Layer Software
Chapter 9 - Performance Evaluation
Appendix A - Formal Definitions for Deadlock Avoidance
Appendix B - Acronyms
References
Index

Interconnection Networks

Ocelot is a dynamic compilation framework designed to map the explicitly data parallel execution model used by NVIDIA CUDA applications onto diverse multithreaded platforms. Ocelot includes a dynamic binary translator from Parallel Thread eXecution ISA (PTX) to many-core processors that leverages the Low Level Virtual Machine (LLVM) code generator to target x86 and other ISAs. The dynamic compiler is able to execute existing CUDA binaries without recompilation from source and supports switching between execution on an NVIDIA GPU and a many-core CPU at runtime. It has been validated against over 130 applications taken from the CUDA SDK, the UIUC Parboil benchmarks [1], the Virginia Rodinia benchmarks [2], the GPU-VSIPL signal and image processing library [3], the Thrust library [4], and several domain specific applications. This paper presents a high level overview of the implementation of the Ocelot dynamic compiler highlighting design decisions and trade-offs, and showcasing their effect on application performance. Several novel code transformations are explored that are applicable only when compiling explicitly parallel applications and traditional dynamic compiler optimizations are revisited for this new class of applications. This study is expected to inform the design of compilation tools for explicitly parallel programming models (such as OpenCL) as well as future CPU and GPU architectures.

/pdf/ocelot-a-dynamic-optimization-framework-for-bulk-synchronous-459dp4ziq0.pdf

Sudhakar Yalamanchili

Papers

Interconnection Networks: An Engineering Approach

Interconnection Networks: An Engineering Approach

Neurocube: a programmable digital neuromorphic architecture with high-density 3D memory

Interconnection Networks

Ocelot: a dynamic optimization framework for bulk-synchronous applications in heterogeneous systems