Author
Dhabaleswar K. Panda
Other affiliations: Chung Hua University, Lincoln University (Pennsylvania), Los Alamos National Laboratory ...read more
Bio: Dhabaleswar K. Panda is an academic researcher from Ohio State University. The author has contributed to research in topics: InfiniBand & Remote direct memory access. The author has an hindex of 56, co-authored 528 publications receiving 13125 citations. Previous affiliations of Dhabaleswar K. Panda include Chung Hua University & Lincoln University (Pennsylvania).
Papers published on a yearly basis
Papers
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23 Jun 2003TL;DR: A new design of MPI over InfiniBand which brings the benefit of RDMA to not only large messages, but also small and control messages and achieves better scalability by exploiting application communication pattern and combining send/receive operations with RDMA operations.
Abstract: Although InfiniBand Architecture is relatively new in the high performance computing area, it offers many features which help us to improve the performance of communication subsystems. One of these features is Remote Direct Memory Access (RDMA) operations. In this paper, we propose a new design of MPI over InfiniBand which brings the benefit of RDMA to not only large messages, but also small and control messages. We also achieve better scalability by exploiting application communication pattern and combining send/receive operations with RDMA operations. Our RDMA-based MPI implementation currently delivers a latency of 6.8 microseconds for small messages and a peak bandwidth of 871 Million Bytes (831 Mega Bytes) per second. Performance evaluation at the MPI level shows that for small messages, our RDMA-based design can reduce the latency by 24%, increase the bandwidth by over 104%, and reduce the host overhead by up to 22%. For large messages, we improve performance by reducing the time for transferring control messages. We have also shown that our new design is beneficial to MPI collective communication and NAS Parallel Benchmarks.
383 citations
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28 Jun 2006TL;DR: A case for HPC with virtual machines is presented by introducing a framework which addresses the performance and management overhead associated with VM-based computing and shows that HPC applications can achieve almost the same performance as those running in a native, non-virtualized environment.
Abstract: Virtual machine (VM) technologies are experiencing a resurgence in both industry and research communities. VMs offer many desirable features such as security, ease of management, OS customization, performance isolation, check-pointing, and migration, which can be very beneficial to the performance and the manageability of high performance computing (HPC) applications. However, very few HPC applications are currently running in a virtualized environment due to the performance overhead of virtualization. Further, using VMs for HPC also introduces additional challenges such as management and distribution of OS images.In this paper we present a case for HPC with virtual machines by introducing a framework which addresses the performance and management overhead associated with VM-based computing. Two key ideas in our design are: Virtual Machine Monitor (VMM) bypass I/O and scalable VM image management. VMM-bypass I/O achieves high communication performance for VMs by exploiting the OS-bypass feature of modern high speed interconnects such as Infini-Band. Scalable VM image management significantly reduces the overhead of distributing and managing VMs in large scale clusters. Our current implementation is based on the Xen VM environment and InfiniBand. However, many of our ideas are readily applicable to other VM environments and high speed interconnects.We carry out detailed analysis on the performance and management overhead of our VM-based HPC framework. Our evaluation shows that HPC applications can achieve almost the same performance as those running in a native, non-virtualized environment. Therefore, our approach holds promise to bring the benefits of VMs to HPC applications with very little degradation in performance.
352 citations
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30 May 2006TL;DR: VMM-bypass allows time-critical I/O operations to be carried out directly in guest VMs without involvement of the VMM and/or a privileged VM by exploiting the intelligence found in modern high speed network interfaces.
Abstract: Currently, I/O device virtualization models in virtual machine (VM) environments require involvement of a virtual machine monitor (VMM) and/or a privileged VM for each I/O operation, which may turn out to be a performance bottleneck for systems with high I/O demands, especially those equipped with modern high speed interconnects such as InfiniBand.
In this paper, we propose a new device virtualization model called VMM-bypass I/O, which extends the idea of OS-bypass originated from user-level communication. Essentially, VMM-bypass allows time-critical I/O operations to be carried out directly in guest VMs without involvement of the VMM and/or a privileged VM. By exploiting the intelligence found in modern high speed network interfaces, VMM-bypass can significantly improve I/O and communication performance for VMs without sacrificing safety or isolation.
To demonstrate the idea of VMM-bypass, we have developed a prototype called Xen-IB, which offers InfiniBand virtualization support in the Xen 3.0 VM environment. Xen-IB runs with current InfiniBand hardware and does not require modifications to existing user-level applications or kernel-level drivers that use InfiniBand. Our performance measurements show that Xen-IB is able to achieve nearly the same raw performance as the original InfiniBand driver running in a non-virtualized environment.
330 citations
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10 Nov 2001TL;DR: This paper proposes Ethernet Message Passing (EMP), a completely new zero-copy, OS-bypass messaging layer for Gigabit Ethernet on Alteon NICs where the entire protocol processing is done at the NIC.
Abstract: Modern interconnects like Myrinet and Gigabit Ethernet offer Gb/s speeds which has put the onus of reducing the communication latency on messaging software. This has led to the development of OS bypass protocols which removed the kernel from the critical path and hence reduced the end-to-end latency. With the advent of programmable NICs, many aspects of protocol processing can be offloaded from user space to the NIC leaving the host processor to dedicate more cycles to the application. Many host-offload messaging systems exist for Myrinet; however, nothing similar exits for Gigabit Ethernet. In this paper we propose Ethernet Message Passing (EMP), a completely new zero-copy, OS-bypass messaging layer for Gigabit Ethernet on Alteon NICs where the entire protocol processing is done at the NIC. This messaging system delivers very good performance (latency of 23 us, and throughput of 880 Mb/s). To the best of our knowledge, this is the first NIC-level implementation of a zero-copy message passing layer for Gigabit Ethernet.
243 citations
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14 May 2007TL;DR: From the message distribution experiments, it is found that on an average about 50% messages are transferred through intra-node communication, which is much higher than intuition, and indicates that optimizing intra- node communication is as important as optimizing inter- nodes communication in a multi-core cluster.
Abstract: Multi-core processors are growing as a new industry trend as single core processors rapidly reach the physical limits of possible complexity and speed. In the new Top500 supercomputer list, more than 20% processors belong to the multi-core processor family. However, without an in-depth study on application behaviors and trends on multi-core clusters, we might not be able to understand the characteristics of multi-core cluster in a comprehensive manner and hence not be able to get optimal performance. In this paper, we take on these challenges and design a set of experiments to study the impact of multi-core architecture on cluster computing. We choose to use one of the most advanced multi-core servers, Intel Bensley system with Woodcrest processors, as our evaluation platform, and use benchmarks including HPL, NAMD, and NAS as the applications to study. From our message distribution experiments, we find that on an average about 50% messages are transferred through intra-node communication, which is much higher than intuition. This trend indicates that optimizing intra- node communication is as important as optimizing inter- node communication in a multi-core cluster. We also observe that cache and memory contention may be a potential bottleneck in multi-core clusters, and communication middleware and applications should be multi-core aware to alleviate this problem. We demonstrate that multi-core aware algorithm, e.g. data tiling, improves benchmark execution time by up to 70%. We also compare the scalability of a multi-core cluster with that of a single-core cluster and find that the scalability of the multi-core cluster is promising.
183 citations
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01 May 1993
TL;DR: 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.
Abstract: 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.
29,323 citations
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TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
Abstract: Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
9,627 citations
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01 Jan 2004
TL;DR: 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, allowing a designer to see all the steps of the process from abstract design to concrete implementation.
Abstract: 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
3,233 citations
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University of Barcelona1, University of the Basque Country2, Technical University of Denmark3, Malmö University4, University of Copenhagen5, SINTEF6, Aarhus University7, Brown University8, University of Wisconsin-Madison9, University of Warwick10, Carnegie Mellon University11, Purdue University12, Karlsruhe Institute of Technology13, ETH Zurich14, University of Freiburg15
TL;DR: The atomic simulation environment (ASE) provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.
Abstract: The Atomic Simulation Environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simula- tions. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple "for-loop" construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.
2,282 citations
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11 Nov 2006TL;DR: This work presents several new algorithms and implementation techniques that significantly accelerate parallel MD simulations compared with current state-of-the-art codes, including a novel parallel decomposition method and message-passing techniques that reduce communication requirements, as well as novel communication primitives that further reduce communication time.
Abstract: Although molecular dynamics (MD) simulations of biomolecular systems often run for days to months, many events of great scientific interest and pharmaceutical relevance occur on long time scales that remain beyond reach. We present several new algorithms and implementation techniques that significantly accelerate parallel MD simulations compared with current stateof- the-art codes. These include a novel parallel decomposition method and message-passing techniques that reduce communication requirements, as well as novel communication primitives that further reduce communication time. We have also developed numerical techniques that maintain high accuracy while using single precision computation in order to exploit processor-level vector instructions. These methods are embodied in a newly developed MD code called Desmond that achieves unprecedented simulation throughput and parallel scalability on commodity clusters. Our results suggest that Desmond?s parallel performance substantially surpasses that of any previously described code. For example, on a standard benchmark, Desmond?s performance on a conventional Opteron cluster with 2K processors slightly exceeded the reported performance of IBM?s Blue Gene/L machine with 32K processors running its Blue Matter MD code.
2,035 citations