as part of your decision process ● direction@lists.isocpp.org. © 2021. 13 What is in Concurrency TS2? • Several synchronization primitives for locked-free programming on concurrent data structures. These Proposed Wording for Concurrent Data Structures: Read-Copy-Update (RCU) 13 Concurrency TS2 in future Concurrency TS2 is an ongoing WIP but might contain the following which has been making its way through implementation status? • Did the TS process work for us? 16 © 2021. 17 Hazard Pointers in Concurrency TS2, C++26, and beyond 17 © 2021. 18 Hazard Pointers in a Nutshell Used to protect access to objects

Reclamation Beyond Concurrency TS2 – Maged Michael Protector Remover / Reclaimer Hazard pointers protect access to objects that may be removed concurrently. SAFE RECLAMATION Concurrency TS2 Essential Hazard Pointer Hazard Pointer Synchronous Reclamation Beyond Concurrency TS2 – Maged Michael See N4895 (wg21.link/n4895) for working draft of Concurrency TS2 Example Using Hazard Pointers class Foo : public hazard_ data, frequently-used, infrequently-updated Hazard Pointer Synchronous Reclamation Beyond Concurrency TS2 – Maged Michael Why use hazard pointers? • Fast (low nanoseconds) and scalable protection. • Supports

garbage that has accumulated Common implementations: • Hazard pointers (Concurrency TS2) • RCU (Concurrency TS2)34 Daniel Anderson -- danielanderson.net Solution #3: Deferred reclamation First

PARTITIONS 3 ( 4. PARTITION P1 VALUES LESS THAN (2), 5. PARTITION P2 VALUES LESS THAN (4) TABLESPACE ts2, 6. PARTITION P3 VALUES LESS THAN (6) TABLESPACE ts3); 使用 checker 进行检查，会报错，表明我们没法迁移 t_error 这张表。 (PARTITION P1 VALUES LESS THAN (2) ENGINE = InnoDB, 8. PARTITION P2 VALUES LESS THAN (4) TABLESPACE = ts2 ENGINE = InnoDB, 9. PARTITION P3 VALUES LESS THAN (6) TABLESPACE = ts3 ENGINE = InnoDB) */" (total (PARTITION P1 VALUES LESS THAN (2) ENGINE = InnoDB, 18. PARTITION P2 VALUES LESS THAN (4) TABLESPACE = ts2 ENGINE = InnoDB, 19. PARTITION P3 VALUES LESS THAN (6) TABLESPACE = ts3 ENGINE = InnoDB) */ error

[102]: rng = DateRange(’1/3/2000’, periods=8) In [103]: ts = Series(randn(8), index=rng) In [104]: ts2 = ts[[0, 3, 6]] In [105]: ts Out[105]: 2000-01-03 -0.889992 2000-01-04 -1.089820 2000-01-05 1.092135 Essential basic functionality pandas: powerful Python data analysis toolkit, Release 0.7.1 In [106]: ts2 Out[106]: 2000-01-03 -0.889992 2000-01-06 -0.713175 2000-01-11 0.459747 In [107]: ts2.reindex(ts pandas: powerful Python data analysis toolkit, Release 0.7.1 Examples >>> d = {’col1’: ts1, ’col2’: ts2} >>> df = DataFrame(data=d, index=index) >>> df2 = DataFrame(np.random.randn(10, 5)) >>> df3 = DataFrame(np

[102]: rng = DateRange(’1/3/2000’, periods=8) In [103]: ts = Series(randn(8), index=rng) In [104]: ts2 = ts[[0, 3, 6]] In [105]: ts Out[105]: 2000-01-03 0.860946 2000-01-04 -0.907973 2000-01-05 -0.295678 Essential basic functionality pandas: powerful Python data analysis toolkit, Release 0.7.2 In [106]: ts2 Out[106]: 2000-01-03 0.860946 2000-01-06 -0.294099 2000-01-11 1.280126 In [107]: ts2.reindex(ts pandas: powerful Python data analysis toolkit, Release 0.7.2 Examples >>> d = {’col1’: ts1, ’col2’: ts2} >>> df = DataFrame(data=d, index=index) >>> df2 = DataFrame(np.random.randn(10, 5)) >>> df3 = DataFrame(np

coroutine, atomic ref, • C++23 senders and receivers? • C++26 executors networking?, concurrency TS2? Conclusion and Future plan • 2021: plan to integrate with MISRA 202X NEXT release • 17 rules to MISRA

[102]: rng = DateRange(’1/3/2000’, periods=8) In [103]: ts = Series(randn(8), index=rng) In [104]: ts2 = ts[[0, 3, 6]] In [105]: ts Out[105]: 2000-01-03 -0.759826 2000-01-04 1.154537 2000-01-05 -0.199039 Essential basic functionality pandas: powerful Python data analysis toolkit, Release 0.7.3 In [106]: ts2 Out[106]: 2000-01-03 -0.759826 2000-01-06 -0.261202 2000-01-11 -0.725007 In [107]: ts2.reindex(ts from_items from sequence of (key, value) pairs read_csv Examples >>> d = {’col1’: ts1, ’col2’: ts2} >>> df = DataFrame(data=d, index=index) >>> df2 = DataFrame(np.random.randn(10, 5)) >>> df3 = DataFrame(np

Integer 不同。 起类型声明作用的语句构造，它们每次出现都声明一种不同的类型，所以下面的声明 type TS1 = set of Char; TS2 = set of Char; 创建了两种不同的类型：TS1 和 TS2。类似的，变量声明 var S1: string[10]; S2: string[10]; 创建两个不同的变量。要创建相同类型的变量，使用

date_range('1/3/2000', periods=8) In [225]: ts = pd.Series(np.random.randn(8), index=rng) In [226]: ts2 = ts[[0, 3, 6]] In [227]: ts (continues on next page) 2.4. Essential basic functionality 111 pandas: 2000-01-08 1.226876 2000-01-09 0.133309 2000-01-10 -0.235413 Freq: D, dtype: float64 In [228]: ts2 Out[228]: 2000-01-03 -2.284837 2000-01-06 -2.345420 2000-01-09 0.133309 dtype: float64 In [229]: AxesSubplot at 0x7f80690fdbd0> Index aware interpolation is available via the method keyword: In [67]: ts2 Out[67]: 2000-01-31 0.469112 2000-02-29 NaN 2002-07-31 -5.785037 2005-01-31 NaN 2008-04-30 -9.011531