Is Parallel Programming Hard, And, If So, What Can You Do About It?

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68 CHAPTER 6. LOCKING1 int delete(int key)2 {3 int b;4 struct element *p;56 b = hashfunction(key);7 p = hashtable[b];8 if (p == NULL || p->key != key)9 return 0;10 spin_lock(&p->lock);11 hashtable[b] = NULL;12 spin_unlock(&p->lock);13 kfree(p);14 return 1;15 }can be solved, in fact, such solutions form the basisof lock-based software transactional memory implementations[ST95, DSS06]. However, Section 8.3.2.5describes a simpler way of providing existence guaranteesfor read-mostly data structures.Figure 6.1: Per-Element Locking Without ExistenceGuarantees1 int delete(int key)2 {3 int b;4 struct element *p;5 spinlock_t *sp;67 b = hashfunction(key);8 sp = &locktable[b];9 spin_lock(sp);10 p = hashtable[b];11 if (p == NULL || p->key != key) {12 spin_unlock(sp);13 return 0;14 }15 hashtable[b] = NULL;16 spin_unlock(sp);17 kfree(p);18 return 1;19 }Figure 6.2: Per-Element Locking With Lock-BasedExistence GuaranteesExistence guarantees are extremely helpful in caseswhere a data element is to be protected by a lockor reference count that is located within the dataelement in question. Code failing to provide suchguarantees is subject to subtle races, as shown inFigure 6.1.Quick Quiz 6.1: <strong>What</strong> if the element we needto delete is not the first element of the list on line 8of Figure 6.1?Quick Quiz 6.2: <strong>What</strong> race condition can occurin Figure 6.1?One way to fix this example is to use a hashedset of global locks, so that each hash bucket has itsown lock, as shown in Figure 6.2. This approachallows acquiring the proper lock (on line 9) beforegaining a pointer to the data element (on line 10).Althoughthisapproachworksquitewellforelementscontained in a single partitionable data structuresuch as the hash table shown in the figure, it can beproblematicifagivendataelementcanbeamemberof multiple hash tables or given more-complex datastructures such as trees or graphs. These problems
Chapter 7Data OwnershipPer-CPU and per-task/process/thread data.Function shipping vs. data shipping.Big question: how much local vs. global processing?How frequent, how expensive, ... Better todivide or to centralize?Relationship to map/reduce? Message passing!@@@ populate with problems showing benefitsof coupling data ownership with other approaches.For example, work-stealing schedulers. Perhaps alsomove memory allocation here, though its current locationis quite good.
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Is Parallel Programming Hard, And,
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Contents1 Introduction 11.1 Histori
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CONTENTSv6 Locking 676.1 Staying Al
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CONTENTSviiB Synchronization Primit
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CONTENTSixE.7.1 Introduction to Pre
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PrefaceThe purpose of this book is
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Chapter 1IntroductionParallel progr
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1.2. PARALLEL PROGRAMMING GOALS 3CP
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1.3. ALTERNATIVES TO PARALLEL PROGR
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1.4. WHAT MAKES PARALLEL PROGRAMMIN
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1.5. GUIDE TO THIS BOOK 9other hand
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Chapter 2Hardware and its HabitsMos
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2.1. OVERVIEW 13Therefore, as shown
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16 CHAPTER 2. HARDWARE AND ITS HABI
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Page 42 and 43: 30 CHAPTER 4. COUNTING1 atomic_t co
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Page 60 and 61: 48 CHAPTER 5. PARTITIONING AND SYNC
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118 CHAPTER 12. ADVANCED SYNCHRONIZ
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138 CHAPTER 13. EASE OF USEFigure 1
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140 CHAPTER 13. EASE OF USE
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142 CHAPTER 14. TIME MANAGEMENT
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144 CHAPTER 15. CONFLICTING VISIONS
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154 APPENDIX A. IMPORTANT QUESTIONS
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156 APPENDIX A. IMPORTANT QUESTIONS
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158 APPENDIX B. SYNCHRONIZATION PRI
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160 APPENDIX B. SYNCHRONIZATION PRI
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162 APPENDIX C. WHY MEMORY BARRIERS
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184 APPENDIX D. READ-COPY UPDATE IM
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244 APPENDIX E. FORMAL VERIFICATION
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272 APPENDIX F. ANSWERS TO QUICK QU
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330 APPENDIX G. GLOSSARY(2) A physi
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332 APPENDIX G. GLOSSARYnear by. Th
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334 APPENDIX G. GLOSSARY
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336 BIBLIOGRAPHY[But97]USA, March 2
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338 BIBLIOGRAPHY[HMB06][Hol03][HP95
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340 BIBLIOGRAPHY[McK06] Paul E. McK
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342 BIBLIOGRAPHYtor. Software - Pra
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344 BIBLIOGRAPHY[UoC08][VGS08]Berke
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346 APPENDIX H. CREDITSH.4 Original
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