From: ko1@... Date: 2020-10-30T00:17:39+00:00 Subject: [ruby-core:100662] [Ruby master Feature#17261] Software transactional memory (STM) for Threads and Ractors Issue #17261 has been updated by ko1 (Koichi Sasada). chrisseaton (Chris Seaton) wrote in #note-14: > I think there's benefits to building STM into the language (if we decided we want STM at all) rather than it being a library. I think so, especially with Ractors, but now we (*I*) don't have enough evidence to persuade, so we can ask later with use cases. > When you look at more advanced features like conflict resolution and conflict mitigation you may want to do more lower level things like control scheduling. Yeah it will be fun work. ---------------------------------------- Feature #17261: Software transactional memory (STM) for Threads and Ractors https://bugs.ruby-lang.org/issues/17261#change-88302 * Author: ko1 (Koichi Sasada) * Status: Rejected * Priority: Normal ---------------------------------------- ## Abstract I propose Software transactional memory (STM) for threads and ractors. Implementation is here: https://github.com/ruby/ruby/pull/3652 The interface is similar to concurrent-ruby, but not the same. http://ruby-concurrency.github.io/concurrent-ruby/1.1.4/Concurrent/TVar.html ## Basic concept https://en.wikipedia.org/wiki/Software_transactional_memory Transaction is popular idea on data base systems to keep state consistency. STM is similar idea to implement optimistic synchronization strategy. There are several advantages compare with traditional synchronization techniques like Mutex and so on: * Performance: in some cases, it is faster because of optimistic nature. * Composability: multiple locks can introduce dead-lock. STM allows nested transaction. In other words, (some kind of) STM can guarantee the progressiveness. The disadvantages is, it can lead slow down on high-contention cases. ## API * `Thread::atomically do expr end`: make a new transaction and run `expr` in it. `expr` can be retried if the conflict is detected. * `Thread::TVar.new(default_value)` * `Thread::TVar#value`: get current value of TVar * `Thread::TVar#value = val`: set TVar value `val`. * `Thread::TVar#increment(n=1)`: Just same as `Thread.atomically{ tv.value += 1 }`. Note that `expr` for `Thread.atomically` can retries and all `TVar#value=` (set TVar values) are reverted before retries. Another operations such as other memory modification, IO operations includes network operations etc are not reverted. The very difference between `Concurrent::TVar` is: * TVar only refer to shareable objects to support Ractor. * `TVar#value=` should be used with `atomically`. We can define as `Thread.atomically{ tv.value = val }`, but it can lead misusing without `atomically`. * `TVar#increment` is special case to allow setting without `atomically` to support typical single counter cases. ## Implementation https://github.com/ruby/ruby/pull/3652 The implementation is almost same as TL2, lock-based STM with global version clock with pthread/win32 threads. We can use atomic operations but not supported yet (but only a few performance benefit on my measuremnets). ## Example ```ruby N = 1_000_000 tv1 = Thread::TVar.new(0) tv2 = Thread::TVar.new(0) r1 = Ractor.new tv1, tv2 do |tv1, tv2| loop do Thread.atomically do v1, v2 = tv1.value, tv2.value raise if v1 != v2 end end end rs = 3.times.map do Ractor.new tv1, tv2 do |tv1, tv2| N.times do Thread.atomically do tv1.value += 1 tv2.value += 1 end end end end rs.each{|r| r.take} p [tv1.value, tv2.value] #=> [3000000, 3000000] ``` In this case, * all `atomically` blocks keep consistency that `tv1.value == tv2.value`. * the results `[3000000, 3000000]` shows consistency on `+=1`. Here is famous bank-account example: ```ruby class Account COUNT = Thread::TVar.new 0 def initialize deposit = 0 @i = COUNT.increment @balance = Thread::TVar.new(deposit) end def transfer_from acc, n Thread::atomically do acc.withdraw n self.deposit n end end def transfer_to acc, n Thread::atomically do self.withdraw n acc.deposit n end end def withdraw n @balance.value -= n end def deposit n @balance.value += n end def balance @balance.value end end AN = 1_0000 N = 10_000_000 RN = 10 iter = 0 accs = AN.times.map{Account.new.freeze}.freeze require 'benchmark' # :forward # two ractors operate N times: a[i].transfer(a[i+1]) # R1: a1->a2, a2->a3, ... # R2: a1->a2, a2->a3, ... # :reverse # two ractors operate N times: a[i].transfer(a[i+1]), # but the oroder of accounts are reversed. # R1: a1->a2, a2->a3, ... # R2: a1->aN-1, a2->aN-2, ... # :shuffle # RN ractors operate N times: a[rand].transfer(a[rand]) # It simulates normal bank-operation mode = :shuffle loop do iter += 1 btime = Time.now case mode when :forward rs = [] rs << Ractor.new(accs) do |accs| N.times{|i| a1, a2 = accs[i%accs.size], accs[(i+1)%accs.size] a1.transfer_to(a2, 1) } end rs << Ractor.new(accs) do |accs| N.times{|i| a1, a2 = accs[i%accs.size], accs[(i+1)%accs.size] a1.transfer_from(a2, 1) } end rs.each{|r| r.take} when :reverse rs = [] rs << Ractor.new(accs) do |accs| N.times{|i| a1, a2 = accs[i%accs.size], accs[(i+1)%accs.size] a1.transfer_to(a2, 1) } end rs << Ractor.new(accs.reverse.freeze) do |accs| N.times{|i| a1, a2 = accs[i%accs.size], accs[(i+1)%accs.size] a1.transfer_from(a2, 1) } end rs.each{|r| r.take} when :shuffle RN.times.map{ Ractor.new(accs) do |accs| rnd = Random.new N.times{ a1 = accs.sample random: rnd a2 = accs.sample random: rnd redo if a1 == a2 a1.transfer_to(a2, rnd.rand(1000)) } end }.each{|r| r.take} else raise end sum = accs.inject(0){|r, acc| acc.balance + r} if sum != 0 pp accs raise "iter #{iter} sum:#{sum}" end etime = Time.now p time: etime - btime # break end ``` This program create AN bank accounts and repeat N transafer operations. You can observe that huge AN reduces conflicts and the execution time is low. Small AN reduces conflicts -> many retries and the execution time is high. ``` AN Execution time (s) Retry counts 100 6.914 958,969 1_000 3.107 186,267 10_000 2.549 26,183 100_000 2.627 2,458 ``` Now x10 retries doesn't affect execution time x10, this is because the current Ractor implementation (acquiring a global lock to raise an exception, and it reduces the retry counts). If we improve the Ractor's implementation, the result would be more worse. ## Consideration ### `Thread.atomically` in ractors At first, I implemented this feature with `Ractor::atomically` and `Ractor::TVar`. However, this STM feature will help the thread programming. This is why I moved from `Ractor::atomically` to `Thread::atomically`. Introduce `Concurrent` namespace what concurrent-ruby are using. However, there are small differences so that I'm not sure is is feasible. Another idea is to support alias: `Thread.atomically` and `Ractor.atomically`. ### `Thread::TVar` can refer only shareable objects Threads can access all objects so we don't need to restrict by such rule. However, to support ractors, this restriction is needed. One idea is separate `Thread::TVar` and `Ractor::TVar`, but it can introduce confusion. Only with shareable objects, thread programs become more thread-safe, so I think it is good choice to have current restriction. ### Bug detection Similar to locking, we can forget to use a `atomically` like that: ```ruby class C def initialize @tv1 = Thread::TVar.new(0) @tv2 = Thread::TVar.new(0) end def tv1() = @tv1.value def tv2() = @tv2.value def tv1 = (v) Thread.atomically{ @tv1.value = v } end def tv2 = (v) Thread.atomically{ @tv2.value = v } end end obj = C.new obj.tv1 += 1 obj.tv2 += 2 ``` It works but it can introduce inconsistency if tv1 and tv2 are tightly coupled with because tv1 and tv2 are not accessed in the same transaction. If tv1 and tv2 need to be modified consistently, we need to write like the following: ```ruby Thread.atomically do obj.tv1 += 1 obj.tv2 += 1 end ``` and `tv1/tv2/tv1=/tv2=` methods should not be defined. I mean we can write bad programs easily. It is same situation with traditional locking (we need to use `Mutex` appropriately). The duty to use it correctly is for programmer. There are some advantages compared with traditional locking: * We can concentrate on TVars. On traditional thread programming we need to check all memory state. * We can introduce logging mechanism and we can find wrong usage (for example: tv1 and tv2 are set within independent transactions). I think we can make some checker based on the log. On traditional thread programming, there are several similar works, but it is difficult to check it because the target of state is most of memory operations. ## Related works * There are many STM implementation techniques. https://www.morganclaypool.com/doi/abs/10.2200/S00070ED1V01Y200611CAC002 * Concurrent Haskell and Clojure are famous to support STM in language (I think). * The model of STM is similar to Clojure. * Clojure allows to access TVar (`ref` in Clojure) value without `atomically` (`dosync` in Clojure). * Clojure doesn't allow to set TVar value without `atomically`. * The API is similar to Concurrent Haskell (`TVar` and `atomically`. * Concurrent-ruby has `Concurrent::TVar`. * But it allows to have an unshareable object. * But is allows to set the value with `atomically`. -- https://bugs.ruby-lang.org/ Unsubscribe: