DOCSLIB.ORG

Routing on the Channel Dependency Graph

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Routing on the Channel Dependency Graph TECHNISCHE UNIVERSITÄT DRESDEN FAKULTÄT INFORMATIK INSTITUT FÜR TECHNISCHE INFORMATIK PROFESSUR FÜR RECHNERARCHITEKTUR Routing on the Channel Dependency Graph: A New Approach to Deadlock-Free, Destination-Based, High-Performance Routing for Lossless Interconnection Networks Dissertation zur Erlangung des akademischen Grades Doktor rerum naturalium (Dr. rer. nat.) vorgelegt von Name: Domke Vorname: Jens geboren am: 12.09.1984 in: Bad Muskau Tag der Einreichung: 30.03.2017 Tag der Verteidigung: 16.06.2017 Gutachter: Prof. Dr. rer. nat. Wolfgang E. Nagel, TU Dresden, Germany Professor Tor Skeie, PhD, University of Oslo, Norway Multicast loops are bad since the same multicast packet will go around and around, inevitably creating a black hole that will destroy the Earth in a fiery conflagration. — OpenSM Source Code iii Abstract In the pursuit for ever-increasing compute power, and with Moore’s law slowly coming to an end, high- performance computing started to scale-out to larger systems. Alongside the increasing system size, the interconnection network is growing to accommodate and connect tens of thousands of compute nodes. These networks have a large influence on total cost, application performance, energy consumption, and overall system efficiency of the supercomputer. Unfortunately, state-of-the-art routing algorithms, which define the packet paths through the network, do not utilize this important resource efficiently. Topology- aware routing algorithms become increasingly inapplicable, due to irregular topologies, which either are irregular by design, or most often a result of hardware failures. Exchanging faulty network components potentially requires whole system downtime further increasing the cost of the failure. This management approach becomes more and more impractical due to the scale of today’s networks and the accompanying steady decrease of the mean time between failures. Alternative methods of operating and maintaining these high-performance interconnects, both in terms of hardware- and software-management, are necessary to mitigate negative effects experienced by scientific applications executed on the supercomputer. However, existing topology-agnostic routing algorithms either suffer from poor load balancing or are not bounded in the number of virtual channels needed to resolve deadlocks in the routing tables. Using the fail-in-place strategy, a well-established method for storage systems to repair only critical component failures, is a feasible solution for current and future HPC interconnects as well as other large-scale installations such as data center networks. Although, an appropriate combination of topology and routing algorithm is required to minimize the throughput degradation for the entire system. This thesis contributes a network simulation toolchain to facilitate the process of finding a suitable combination, either during system design or while it is in operation. On top of this foundation, a key contribution is a novel scheduling-aware routing, which reduces fault-induced throughput degradation while improving overall network utilization. The scheduling-aware routing performs frequent property preserving routing updates to optimize the path balancing for simultaneously running batch jobs. The increased deployment of lossless interconnection networks, in conjunction with fail-in-place modes of operation and topology-agnostic, scheduling-aware routing algorithms, necessitates new solutions to solve the routing-deadlock problem. Therefore, this thesis further advances the state-of-the-art by introducing a novel concept of routing on the channel dependency graph, which allows the design of an universally applicable destination-based routing capable of optimizing the path balancing without exceeding a given number of virtual channels, which are a common hardware limitation. This disruptive innovation enables implicit deadlock-avoidance during path calculation, instead of solving both problems separately as all previous solutions. v Contents List of Figures xi List of Tables xiii List of Algorithms xv List of Abbreviations and Symbols xvii 1 Introduction 1 1.1 Motivation . .1 1.2 Contributions . .4 1.3 Thesis Organization . .5 2 Related Work 7 2.1 State-of-the-Art for High-Performance Interconnect Designs . .7 2.1.1 Resiliency of Networks, Topologies, and Routings . .7 2.1.2 HPC Routing Strategies and Network Management . .8 2.1.3 Simulation Frameworks for Network Analyses . .9 2.2 State-of-the-Art for Deadlock-free Routing Approaches . 10 2.2.1 Topology-aware Routing Algorithms . 12 2.2.2 Topology-agnostic Routing Algorithms . 12 3 Background, Assumptions, and Definitions 15 3.1 Interconnection Networks and Routing Algorithms . 15 3.1.1 Network-related Definitions . 15 3.1.2 Routing-related Definitions . 17 3.1.3 Topology and Routing Metrics . 19 3.2 Common Network Topologies and Production HPC Systems . 21 3.2.1 Meshes and Tori . 22 3.2.2 Fat-Trees and eXtended Generalized Fat-Trees . 23 3.2.3 Kautz Graph . 24 3.2.4 Dragonfly Topologies . 24 3.2.5 Random Topologies . 26 vii 3.2.6 Real-world HPC Systems . 26 3.3 Selection of Routing Algorithms for HPC . 28 3.3.1 MinHop Routing . 28 3.3.2 Single-Source Shortest-Path Routing (SSSP) . 28 3.3.3 Dimension Order Routing (DOR) . 29 3.3.4 Up*/Down* and Down*/Up* Routing . 29 3.3.5 Fat-Tree Routing . 29 3.3.6 Layered Shortest Path Routing (LASH) . 31 3.3.7 Torus-2QoS Routing . 31 3.3.8 Deadlock-Free SSSP Routing (DFSSSP) . 32 4 Fail-in-Place High-Performance Networks 33 4.1 Failures Analysis for Real HPC Systems . 33 4.2 Building Blocks for Fail-in-Place Networks . 36 4.2.1 Resilient Topologies . 36 4.2.2 Resilient Routing Algorithms . 36 4.2.3 Resiliency Metrics . 37 4.3 Interconnect Simulation Framework . 40 4.3.1 Toolchain Overview . 40 4.3.2 InfiniBand Model in OMNeT++ . 41 4.3.3 Traffic Injection . 42 4.3.4 Simulator Improvements . 43 4.4 Simulation Results . 44 4.4.1 Initial Usability Study . 44 4.4.2 Influence of Link Faults on Small Topologies . 46 4.4.3 Influence of Link Faults on Large Topologies . 50 4.4.4 Case Study of the TSUBAME2 Supercomuter . 52 4.4.5 Case Study of the Deimos HPC System . 54 5 Utilization Improvement through SAR 57 5.1 Conceptual Design of SAR for shared HPC Environments . 57 5.2 Example Implementation of the Scheduling-Aware Routing . 62 5.2.1 Hardware and Software Building Blocks . 62 5.2.2 Scheduling-Aware Routing Optimization . 65 5.2.3 Property Preserving Network Update for InfiniBand . 67 5.3 Evaluation . 71 5.3.1 Current Limitations and Problems . 72 5.3.2 Theoretical Evaluation of Network Metrics . 72 5.3.3 Practical Evaluation on a Production System . 77 viii 6 Routing on the Channel Dependency Graph 81 6.1 Limitations of Multi-Step Routing Approaches . 81 6.2 Nue Routing . 84 6.2.1 Complete Channel Dependency Graph . 84 6.2.2 Escape Paths for the Static Nue Routing . 85 6.2.3 Choosing Root Node for the Spanning Tree . 87 6.2.4 Dijkstra’s Algorithm for the complete CDG . 89 6.2.5 Nue Routing Function . 91 6.2.6 Optimizations for Nue Routing . 92 6.2.7 Correctness, Completeness & Complexity . 99 6.3 Evaluation of Nue Routing . 102 6.3.1 Path Length and Edge Forwarding Index . 103 6.3.2 Throughput for Regular and Irregular Topologies . 104 6.3.3 Runtime and Practical Considerations . 107 7 Summary, Conclusion, and Future Work 109 Bibliography 113 ix List of Figures 1.1 Interconnect technology statistics of the TOP500 list of November 2016 . .2 3.1 5-node interconnection network I = G(N;C) ........................ 16 3.2 Using a shortest-path, counter-clockwise routing for network I induces the channel dependency graph D .................................... 18 3.3 Example visualization of a 3-dimensional mesh and torus topology without terminals . 22 3.4 Example visualization of a k-ary n-tree topology and an extended generalized fat-tree topology . 23 3.5 Example visualization of a Kautz graph and a dragonfly topology . 24 3.6 Topology configurations of three real-world supercomputers . 27 4.1 Network-related hardware failures of three HPC systems . 34 4.2 Whisker plot for lost connections while removing links or switches from a 10-ary 3-tree without performing a rerouting . 39 4.3 Toolchain to evaluate the network throughput of a fail-in-place network . 41 4.4 Whisker plots of consumption bandwidth for uniform random injection . 47 4.5 Histograms for exchange patterns with error bars, showing mean value and the 95% confidence interval for ten seeds . 49 4.6 Histograms for exchange patterns executed on networks with up to 8% link failures . 51 4.7 Histograms for exchange patterns for different failure types using DFSSSP, fat-tree and Up*/Down* routing on TSUBAME2 . 53 4.8 Histograms for exchange patterns for different failure types using MinHop, DFSSSP and Up*/Down* routing on Deimos . 55 5.1 Batch job history of two petascale HPC systems for one month of operation . 58 5.2 Comparison of effective EFI for inter-switch links: Oblivious routing vs. scheduling-aware routing for three equal-sized batch jobs on a 2-level fat-tree . 60 5.3 Qualitative comparison of different routing and network optimizations for supercomputers 61 5.4 SLURM architecture . 63 5.5 Open MPI’s modular component architecture establishes a message passing interface between application and hardware to enable inter-process communication . 64 5.6 Flowchart of a filtering tool . 65 xi 5.7 Sequence diagram of a five-phase update protocol to achieve property preserving network updates of InfiniBand networks . 70 5.8 Replay of job history (Figure 5.1) for two HPC systems . 74 5.9 Heat map of the EFIs for inter-switch links of TSUBAME2 for the 200-node batch job on day 16 of Feb. ’15; Used routing algorithm: fat-tree .
Load more

Recommended publications
  • Deadlock: Why Does It Happen? CS 537 Andrea C
    UNIVERSITY of WISCONSIN-MADISON Computer Sciences Department Deadlock: Why does it happen? CS 537 Andrea C. Arpaci-Dusseau Introduction to Operating Systems Remzi H. Arpaci-Dusseau Informal: Every entity is waiting for resource held by another entity; none release until it gets what it is Deadlock waiting for Questions answered in this lecture: What are the four necessary conditions for deadlock? How can deadlock be prevented? How can deadlock be avoided? How can deadlock be detected and recovered from? Deadlock Example Deadlock Example Two threads access two shared variables, A and B int A, B; Variable A is protected by lock x, variable B by lock y lock_t x, y; How to add lock and unlock statements? Thread 1 Thread 2 int A, B; lock(x); lock(y); A += 10; B += 10; lock(y); lock(x); Thread 1 Thread 2 B += 20; A += 20; A += 10; B += 10; A += B; A += B; B += 20; A += 20; unlock(y); unlock(x); A += B; A += B; A += 30; B += 30; A += 30; B += 30; unlock(x); unlock(y); What can go wrong?? 1 Representing Deadlock Conditions for Deadlock Two common ways of representing deadlock Mutual exclusion • Vertices: • Resource can not be shared – Threads (or processes) in system – Resources (anything of value, including locks and semaphores) • Requests are delayed until resource is released • Edges: Indicate thread is waiting for the other Hold-and-wait Wait-For Graph Resource-Allocation Graph • Thread holds one resource while waits for another No preemption “waiting for” wants y held by • Resources are released voluntarily after completion T1 T2 T1 T2 Circular
    [Show full text]
  • The Dining Philosophers Problem Cache Memory
    The Dining Philosophers Problem Cache Memory 254 The dining philosophers problem: definition It is an artificial problem widely used to illustrate the problems linked to resource sharing in concurrent programming. The problem is usually described as follows. • A given number of philosopher are seated at a round table. • Each of the philosophers shares his time between two activities: thinking and eating. • To think, a philosopher does not need any resources; to eat he needs two pieces of silverware. 255 • However, the table is set in a very peculiar way: between every pair of adjacent plates, there is only one fork. • A philosopher being clumsy, he needs two forks to eat: the one on his right and the one on his left. • It is thus impossible for a philosopher to eat at the same time as one of his neighbors: the forks are a shared resource for which the philosophers are competing. • The problem is to organize access to these shared resources in such a way that everything proceeds smoothly. 256 The dining philosophers problem: illustration f4 P4 f0 P3 f3 P0 P2 P1 f1 f2 257 The dining philosophers problem: a first solution • This first solution uses a semaphore to model each fork. • Taking a fork is then done by executing a operation wait on the semaphore, which suspends the process if the fork is not available. • Freeing a fork is naturally done with a signal operation. 258 /* Definitions and global initializations */ #define N = ? /* number of philosophers */ semaphore fork[N]; /* semaphores modeling the forks */ int j; for (j=0, j < N, j++) fork[j]=1; Each philosopher (0 to N-1) corresponds to a process executing the following procedure, where i is the number of the philosopher.
    [Show full text]
  • CSC 553 Operating Systems Multiple Processes
    CSC 553 Operating Systems Lecture 4 - Concurrency: Mutual Exclusion and Synchronization Multiple Processes • Operating System design is concerned with the management of processes and threads: • Multiprogramming • Multiprocessing • Distributed Processing Concurrency Arises in Three Different Contexts: • Multiple Applications – invented to allow processing time to be shared among active applications • Structured Applications – extension of modular design and structured programming • Operating System Structure – OS themselves implemented as a set of processes or threads Key Terms Related to Concurrency Principles of Concurrency • Interleaving and overlapping • can be viewed as examples of concurrent processing • both present the same problems • Uniprocessor – the relative speed of execution of processes cannot be predicted • depends on activities of other processes • the way the OS handles interrupts • scheduling policies of the OS Difficulties of Concurrency • Sharing of global resources • Difficult for the OS to manage the allocation of resources optimally • Difficult to locate programming errors as results are not deterministic and reproducible Race Condition • Occurs when multiple processes or threads read and write data items • The final result depends on the order of execution – the “loser” of the race is the process that updates last and will determine the final value of the variable Operating System Concerns • Design and management issues raised by the existence of concurrency: • The OS must: – be able to keep track of various processes
    [Show full text]
  • Supervision 1: Semaphores, Generalised Producer-Consumer, and Priorities
    Concurrent and Distributed Systems - 2015–2016 Supervision 1: Semaphores, generalised producer-consumer, and priorities Q0 Semaphores (a) Counting semaphores are initialised to a value — 0, 1, or some arbitrary n. For each case, list one situation in which that initialisation would make sense. (b) Write down two fragments of pseudo-code, to be run in two different threads, that experience deadlock as a result of poor use of mutual exclusion. (c) Deadlock is not limited to mutual exclusion; it can occur any time its preconditions (especially hold-and-wait, cyclic dependence) occur. Describe a situation in which two threads making use of semaphores for condition synchronisation (e.g., in producer-consumer) can deadlock. int buffer[N]; int in = 0, out = 0; spaces = new Semaphore(N); items = new Semaphore(0); guard = new Semaphore(1); // for mutual exclusion // producer threads while(true) { item = produce(); wait(spaces); wait(guard); buffer[in] = item; in = (in + 1) % N; signal(guard); signal(items); } // consumer threads while(true) { wait(items); wait(guard); item = buffer[out]; out =(out+1) % N; signal(guard); signal(spaces); consume(item); } Figure 1: Pseudo-code for a producer-consumer queue using semaphores. 1 (d) In Figure 1, items and spaces are used for condition synchronisation, and guard is used for mutual exclusion. Why will this implementation become unsafe in the presence of multiple consumer threads or multiple producer threads, if we remove guard? (e) Semaphores are introduced in part to improve efficiency under contention around critical sections by preferring blocking to spinning. Describe a situation in which this might not be the case; more generally, under what circumstances will semaphores hurt, rather than help, performance? (f) The implementation of semaphores themselves depends on two classes of operations: in- crement/decrement of an integer, and blocking/waking up threads.
    [Show full text]
  • Q1. Multiple Producers and Consumers
    CS39002: Operating Systems Lab. Assignment 5 Floating date: 29/2/2016 Due date: 14/3/2016 Q1. Multiple Producers and Consumers Problem Definition: You have to implement a system which ensures synchronisation in a producer-consumer scenario. You also have to demonstrate deadlock condition and provide solutions for avoiding deadlock. In this system a main process creates 5 producer processes and 5 consumer processes who share 2 resources (queues). The producer's job is to generate a piece of data, put it into the queue and repeat. At the same time, the consumer process consumes the data i.e., removes it from the queue. In the implementation, you are asked to ensure synchronization and mutual exclusion. For instance, the producer should be stopped if the buffer is full and that the consumer should be blocked if the buffer is empty. You also have to enforce mutual exclusion while the processes are trying to acquire the resources. Manager (manager.c): It is the main process that creates the producers and consumer processes (5 each). After that it periodically checks whether the system is in deadlock. Deadlock refers to a specific condition when two or more processes are each waiting for another to release a resource, or more than two processes are waiting for resources in a circular chain. Implementation : The manager process (manager.c) does the following : i. It creates a file matrix.txt which holds a matrix with 2 rows (number of resources) and 10 columns (ID of producer and consumer processes). Each entry (i, j) of that matrix can have three values: ● 0 => process i has not requested for queue j or released queue j ● 1 => process i requested for queue j ● 2 => process i acquired lock of queue j.
    [Show full text]
  • Deadlock-Free Oblivious Routing for Arbitrary Topologies
    Deadlock-Free Oblivious Routing for Arbitrary Topologies Jens Domke Torsten Hoefler Wolfgang E. Nagel Center for Information Services and Blue Waters Directorate Center for Information Services and High Performance Computing National Center for Supercomputing Applications High Performance Computing Technische Universitat¨ Dresden University of Illinois at Urbana-Champaign Technische Universitat¨ Dresden Dresden, Germany Urbana, IL 61801, USA Dresden, Germany [email protected] [email protected] [email protected] Abstract—Efficient deadlock-free routing strategies are cru- network performance without inclusion of the routing al- cial to the performance of large-scale computing systems. There gorithm or the application communication pattern. In reg- are many methods but it remains a challenge to achieve lowest ular operation these values can hardly be achieved due latency and highest bandwidth for irregular or unstructured high-performance networks. We investigate a novel routing to network congestion. The largest gap between real and strategy based on the single-source-shortest-path routing al- idealized performance is often in bisection bandwidth which gorithm and extend it to use virtual channels to guarantee by its definition only considers the topology. The effective deadlock-freedom. We show that this algorithm achieves min- bisection bandwidth [2] is the average bandwidth for routing imal latency and high bandwidth with only a low number messages between random perfect matchings of endpoints of virtual channels and can be implemented in practice. We demonstrate that the problem of finding the minimal number (also known as permutation routing) through the network of virtual channels needed to route a general network deadlock- and thus considers the routing algorithm.
    [Show full text]
  • Scalable Deadlock Detection for Concurrent Programs
    Sherlock: Scalable Deadlock Detection for Concurrent Programs Mahdi Eslamimehr Jens Palsberg UCLA, University of California, Los Angeles, USA {mahdi,palsberg}@cs.ucla.edu ABSTRACT One possible schedule of the program lets the first thread We present a new technique to find real deadlocks in con- acquire the lock of A and lets the other thread acquire the current programs that use locks. For 4.5 million lines of lock of B. Now the program is deadlocked: the first thread Java, our technique found almost twice as many real dead- waits for the lock of B, while the second thread waits for locks as four previous techniques combined. Among those, the lock of A. 33 deadlocks happened after more than one million com- Usually a deadlock is a bug and programmers should avoid putation steps, including 27 new deadlocks. We first use a deadlocks. However, programmers may make mistakes so we known technique to find 1275 deadlock candidates and then have a bug-finding problem: provide tool support to find as we determine that 146 of them are real deadlocks. Our tech- many deadlocks as possible in a given program. nique combines previous work on concolic execution with a Researchers have developed many techniques to help find new constraint-based approach that iteratively drives an ex- deadlocks. Some require program annotations that typically ecution towards a deadlock candidate. must be supplied by a programmer; examples include [18, 47, 6, 54, 66, 60, 42, 23, 35]. Other techniques work with unannotated programs and thus they are easier to use. In Categories and Subject Descriptors this paper we focus on techniques that work with unanno- D.2.5 Software Engineering [Testing and Debugging] tated Java programs.
    [Show full text]
  • The Deadlock Problem
    The deadlock problem n A set of blocked processes each holding a resource and waiting to acquire a resource held by another process. n Example n locks A and B Deadlocks P0 P1 lock (A); lock (B) lock (B); lock (A) Arvind Krishnamurthy Spring 2004 n Example n System has 2 tape drives. n P1 and P2 each hold one tape drive and each needs another one. n Deadlock implies starvation (opposite not true) The dining philosophers problem Deadlock Conditions n Five philosophers around a table --- thinking or eating n Five plates of food + five forks (placed between each n Deadlock can arise if four conditions hold simultaneously: plate) n Each philosopher needs two forks to eat n Mutual exclusion: limited access to limited resources n Hold and wait void philosopher (int i) { while (TRUE) { n No preemption: a resource can be released only voluntarily think(); take_fork (i); n Circular wait take_fork ((i+1) % 5); eat(); put_fork (i); put_fork ((i+1) % 5); } } Resource-allocation graph Resource-Allocation & Waits-For Graphs A set of vertices V and a set of edges E. n V is partitioned into two types: n P = {P1, P2, …, Pn }, the set consisting of all the processes in the system. n R = {R1, R2, …, Rm }, the set consisting of all resource types in the system (CPU cycles, memory space, I/O devices) n Each resource type Ri has Wi instances. n request edge – directed edge P1 ® Rj Resource-Allocation Graph Corresponding wait-for graph n assignment edge – directed edge Rj ® Pi 1 Deadlocks with multiple resources Another example n P1 is waiting for P2 or P3, P3 is waiting for P1 or P4 P1 is waiting for P2, P2 is waiting for P3, P3 is waiting for P1 or P2 n Is there a deadlock situation here? Is this a deadlock scenario? Announcements Methods for handling deadlocks th n Midterm exam on Feb.
    [Show full text]
  • Deadlock Problem Mutex T M1, M2;
    The deadlock problem mutex_t m1, m2; void p1 (void *ignored) { lock (m1); lock (m2); /* critical section */ unlock (m2); unlock (m1); } void p2 (void *ignored) { lock (m2); lock (m1); /* critical section */ unlock (m1); unlock (m2); } • This program can cease to make progress – how? • Can you have deadlock w/o mutexes? 1/15 More deadlocks • Same problem with condition variables - Suppose resource 1 managed by c1, resource 2 by c2 - A has 1, waits on c2, B has 2, waits on c1 • Or have combined mutex/condition variable deadlock: - lock (a); lock (b); while (!ready) wait (b, c); unlock (b); unlock (a); - lock (a); lock (b); ready = true; signal (c); unlock (b); unlock (a); • One lesson: Dangerous to hold locks when crossing abstraction barriers! - I.e., lock (a) then call function that uses condition variable 2/15 Deadlocks w/o computers • Real issue is resources & how required • E.g., bridge only allows traffic in one direction - Each section of a bridge can be viewed as a resource. - If a deadlock occurs, it can be resolved if one car backs up (preempt resources and rollback). - Several cars may have to be backed up if a deadlock occurs. - Starvation is possible. 3/15 Deadlock conditions 1. Limited access (mutual exclusion): - Resource can only be shared with finite users. 2. No preemption: - once resource granted, cannot be taken away. 3. Multiple independent requests (hold and wait): - don’t ask all at once (wait for next resource while holding current one) 4. Circularity in graph of requests • All of 1–4 necessary for deadlock to occur • Two approaches to dealing with deadlock: - pro-active: prevention - reactive: detection + corrective action 4/15 Prevent by eliminating one condition 1.
    [Show full text]
  • Deadlock Immunity: Enabling Systems to Defend Against Deadlocks
    Deadlock Immunity: Enabling Systems To Defend Against Deadlocks Horatiu Jula, Daniel Tralamazza, Cristian Zamfir, George Candea Dependable Systems Laboratory EPFL Switzerland Abstract new executions that were not covered by prior testing. Deadlock immunity is a property by which programs, Furthermore, modern systems accommodate extensions once afflicted by a given deadlock, develop resistance written by third parties, which can introduce new dead- against future occurrences of that and similar deadlocks. locks into the target systems (e.g., Web browser plugins, We describe a technique that enables programs to auto- enterprise Java beans). matically gain such immunity without assistance from Debugging deadlocks is hard—merely seeing a dead- programmers or users. We implemented the technique lock happen does not mean the bug is easy to fix. for both Java and POSIX threads and evaluated it with Deadlocks often require complex sequences of low- several real systems, including MySQL, JBoss, SQLite, probability events to manifest (e.g., timing or workload Apache ActiveMQ, Limewire, and Java JDK. The results dependencies, presence or absence of debug code, com- demonstrate effectiveness against real deadlock bugs, piler optimization options), making them hard to repro- while incurring modest performance overhead and scal- duce and diagnose. Sometimes deadlocks are too costly ing to 1024 threads. We therefore conclude that deadlock to fix, as they entail drastic redesign. Patches are error- immunity offers programmers and users an attractive tool prone: many concurrency bug fixes either introduce new for coping with elusive deadlocks. bugs or, instead of fixing the underlying bug, merely de- crease the probability of occurrence [16]. 1 Introduction We expect the deadlock challenge to persist and likely Writing concurrent software is one of the most challeng- become worse over time: On the one hand, software ing endeavors faced by software engineers, because it re- systems continue getting larger and more complex.
    [Show full text]
  • Edsger Wybe Dijkstra
    In Memoriam Edsger Wybe Dijkstra (1930–2002) Contents 0 Early Years 2 1 Mathematical Center, Amsterdam 3 2 Eindhoven University of Technology 6 3 Burroughs Corporation 8 4 Austin and The University of Texas 9 5 Awards and Honors 12 6 Written Works 13 7 Prophet and Sage 14 8 Some Memorable Quotations 16 Edsger Wybe Dijkstra, Professor Emeritus in the Computer Sciences Department at the University of Texas at Austin, died of cancer on 6 August 2002 at his home in Nuenen, the Netherlands. Dijkstra was a towering figure whose scientific contributions permeate major domains of computer science. He was a thinker, scholar, and teacher in renaissance style: working alone or in a close-knit group on problems of long-term importance, writing down his thoughts with exquisite precision, educating students to appreciate the nature of scientific research, and publicly chastising powerful individuals and institutions when he felt scientific integrity was being compromised for economic or political ends. In the words of Professor Sir Tony Hoare, FRS, delivered by him at Dijkstra’s funeral: Edsger is widely recognized as a man who has thought deeply about many deep questions; and among the deepest questions is that of traditional moral philosophy: How is it that a person should live their life? Edsger found his answer to this question early in his life: He decided he would live as an academic scientist, conducting research into a new branch of science, the science of computing. He would lay the foundations that would establish computing as a rigorous scientific discipline; and in his research and in his teaching and in his writing, he would pursue perfection to the exclusion of all other concerns.
    [Show full text]
  • Comparing Type Systems for Deadlock Freedom
    Comparing Type Systems for Deadlock Freedom Ornela Dardha Jorge A. Perez´ University of Glasgow, UK University of Groningen, e Netherlands September 7, 2021 Abstract Message-passing soware systems exhibit non-trivial forms of concurrency and distribution; they are expected to follow intended protocols among communicating services, but also to never “get stuck”. is intuitive requirement has been expressed by liveness properties such as progress or (dead)lock freedom and various type systems ensure these properties for concurrent processes. Unfortunately, very lile is known about the precise relationship between these type systems and the classes of typed processes they induce. is paper puts forward the first comparative study of different type systems for message-passing processes that guarantee deadlock freedom. We compare two classes of deadlock-free typed processes, here denoted L and K. e class L stands out for its canonicity: it results from Curry-Howard interpre- tations of classical linear logic propositions as session types. e class K, obtained by encoding session types into Kobayashi’s linear types with usages, includes processes not typable in other type systems. We show that L is strictly included in K, and identify the precise conditions under which they coincide. We also provide two type-preserving translations of processes in K into processes in L. 1 Introduction In this paper, we formally relate different type systems for concurrent processes specified in the π- calculus [MPW92]. A fundamental model of computation, the π-calculus stands out for its expressiveness, which enables us to represent and reason about message-passing programs in functional, object-oriented, and distributed paradigms [SW01].
    [Show full text]