NON-BLOCKING DATA STRUCTURES AND TRANSACTIONAL MEMORY Tim Harris, - PowerPoint PPT Presentation

NON-BLOCKING DATA STRUCTURES AND TRANSACTIONAL MEMORY Tim Harris, 21 November 2014

Lecture 7 � Linearizability � Lock-free progress properties � Queues � Reducing contention � Explicit memory management

Linearizability 3

More generally � Suppose we build a shared-memory data structure directly from read/write/CAS, rather than using locking as an intermediate layer Data structure Data structure Locks H/W primitives: read, H/W primitives: read, write, CAS, ... write, CAS, ... � Why might we want to do this? � What does it mean for the data structure to be correct? 4

What we’re building � A set of integers, represented by a sorted linked list � ����������������� � ������������������� � ������������������� 5

Searching a sorted list � ��������� 20? 30 H 10 T ����������������� 6

Inserting an item with CAS � ����������� 30 → 20 � 30 H 10 T 20 ������������������ 7

Inserting an item with CAS � ����������� � ����������� 30 → 25 � 30 → 20 � 30 H 10 T 20 25 8

Searching and finding together �� ����� � ���������� ������� � �������� ...but this thread 20? This thread saw 20 succeeded in putting was not in the set... it in! 30 H 10 T � Is this a correct implementation of a set? 20 � Should the programmer be surprised if this happens? � What about more complicated mixes of operations? 9

Correctness criteria Informally: Look at the behaviour of the data structure (what operations are called on it, and what their results are). If this behaviour is indistinguishable from atomic calls to a sequential implementation then the concurrent implementation is correct. 10

Sequential specification � Ignore the list for the moment, and focus on the set: 10, 20, 30 Sequential: we’re only Specification: we’re saying what considering one operation a set does, not what a list does, on the set at a time or how it looks in memory insert(15)->true 10, 15, 20, 30 ����������������� ������������������� delete(20)->true insert(20)->false ������������������� 10, 15, 30 10, 15, 20, 30 11

Sequential specification ������������������ 10, 20, 30 deleteany()->10 deleteany()->20 20, 30 10, 30 This is still a sequential spec... just not a deterministic one 12

System model Thread 1 ... Thread n Threads make find/insert/delete invocations and receive responses from the set (~method calls/returns) Shared object (e.g. “set”) ...the set is read/write/CAS implemented by making invocations and responses on memory Primitive objects (e.g. “memory location”) 13

High level: sequential history � No overlapping invocations: T1: insert(10) T2: insert(20) T1: find(15) -> false -> true -> true time 10 10, 20 10, 20 14

High level: concurrent history � Allow overlapping invocations: insert(10)->true insert(20)->true Thread 1: time Thread 2: find(20)->false 15

Linearizability � Is there a correct sequential history: � Same results as the concurrent one � Consistent with the timing of the invocations/responses? 16

Example: linearizable insert(10)->true insert(20)->true Thread 1: time Thread 2: A valid sequential find(20)->false history: this concurrent execution is OK 17

Example: linearizable insert(10)->true delete(10)->true Thread 1: time Thread 2: A valid sequential find(10)->false history: this concurrent execution is OK 18

Example: not linearizable insert(10)->true insert(10)->false Thread 1: time Thread 2: delete(10)->true 19

Returning to our example � ���������� �� ���� � �������� �������� 20? 30 H 10 T A valid sequential history: 20 this concurrent execution is OK find(20)->false Thread 1: Thread 2: insert(20)->true 20

Recurring technique � For updates: � Perform an essential step of an operation by a single atomic instruction � E.g. CAS to insert an item into a list � This forms a “linearization point” � For reads: � Identify a point during the operation’s execution when the result is valid � Not always a specific instruction 21

Correctness (informal) 10, 15, 10, 20 20 Abstraction function maps the concrete list to the abstract set’s contents 20 H 10 T 15 22

Correctness (informal) High-level operation Lookup(20) Insert(15) time � ����� � ����� ��� ������ ���� True True Primitive step (read/write/CAS) 23

Correctness (informal) A left mover commutes with operations immediately before it Lookup(20) Insert(15) A right mover commutes with operations immediately after it time � ����� � ����� ��� ������ ���� Show operations before linearization 1. True True point are right movers Show operations after linearization point 2. are left movers Show linearization point updates abstract state 3. 24

Correctness (informal) A left mover commutes with operations immediately before it Lookup(20) Insert(15) A right mover commutes with operations immediately after it time � ����� ������ � ����� ��� ���� True True Move these right over the read of the 10->20 link 25

Adding “delete” � First attempt: just use CAS delete(10): 10 → 30 � 30 H 10 T 26

Delete and insert: � delete(10) & insert(20): 10 → 30 � 30 → 20 � 30 H 10 T 20 � � � � 27

Logical vs physical deletion � Use a ‘spare’ bit to indicate logically deleted nodes: � � � � 10 → 30 30 → 30X 30 H 10 T 30 → 20 � 20 28

Delete-greater-than-or-equal � DeleteGE(int x) -> int � Remove “x”, or next element above “x” 30 H 10 T � DeleteGE(20) -> 30 H 10 T 29

Does this work: DeleteGE(20) 30 H 10 T 1. Walk down the list, as in a normal delete, find 30 as next-after-20 2. Do the deletion as normal: set the mark bit in 30, then physically unlink 30

Delete-greater-than-or-equal B must be after A (thread order) insert(25)->true insert(30)->false A B Thread 1: time C Thread 2: deleteGE(20)->30 A must be after C C must be after B (otherwise C should (otherwise B should have returned 15) have succeeded) 31

How to realise this is wrong � See operation which determines result � Consider a delay at that point � Is the result still valid? � Delayed read: is the memory still accessible? � Delayed write: is the write still correct to perform? � Delayed CAS: does the value checked by the CAS determine the result? 32

Lock-free progress properties 33

Progress: is this a good “lock-free” list? ������� ����������� !"#$%�&�'��( OK, we’re not calling pthread_mutex_lock... but ������������� )����* we’re essentially doing the same thing ++�,���������������� ������� �-���������.!"#$%�&/��/����''����*� 0 111� ++�2����������� !"#$%�&�'��( 0 34

“Lock-free” � A specific kind of non-blocking progress guarantee � Precludes the use of typical locks � From libraries � Or “hand rolled” � Often mis-used informally as a synonym for � Free from calls to a locking function � Fast � Scalable 35

“Lock-free” � A specific kind of non-blocking progress guarantee � Precludes the use of typical locks � From libraries � Or “hand rolled” � Often mis-used informally as a synonym for � Free from calls to a locking function � Fast � Scalable The version number mechanism is an example of a technique that is often effective in practice, does not use locks, but is not lock-free in this technical sense 36

Wait-free � A thread finishes its own operation if it continues executing steps Start Start Start time Finish Finish Finish 37

Implementing wait-free algorithms � Important in some significant niches � e.g., in real-time systems with worst-case execution time guarantees � General construction techniques exist (“universal constructions”) � Queuing and helping strategies: everyone ensures oldest operation makes progress � Often a high sequential overhead � Often limited scalability � Fast-path / slow-path constructions � Start out with a faster lock-free algorithm � Switch over to a wait-free algorithm if there is no progress � ...if done carefully, obtain wait-free progress overall � In practice, progress guarantees can vary between operations on a shared object � e.g., wait-free find + lock-free delete 38