Changes between Version 132 and Version 133 of LightweightConcurrency

Timestamp:

02/26/13 11:14:53 (3 months ago)

Author:

kc

Comment:

--

LightweightConcurrency

@@ -9 +9 @@
 == Introduction == 
 
-GHC has a rich support for concurrency (forkIO, MVars, STM, Asynchronous exceptions, bound threads, safe FFI, transparent scaling on multicores, etc.) and a fast and robust runtime system. However, the concurrency support is implemented in C and baked into the RTS. The concurrency primitives non-trivially interact among each other, and along with the lightweight thread scheduler, through a cascade of locks and condition variables. Often times, the invariants on which RTS fields can be accessed when are expressed as comments, and enforced through assertions (See [source:rts/Task.h@085c7fe5d4ea6e7b59f944d46ecfeba3755a315b#L37 here] for one such fascinating example). This policy of enforcing through assertions keeps the overheads low, but makes the task of modifying and extending the runtime cumbersome.
+GHC has a rich support for concurrency (forkIO, MVars, STM, Asynchronous exceptions, bound threads, safe FFI, transparent scaling on multicores, etc.) and a fast and robust runtime system. However, the concurrency support is implemented in C and baked into the RTS. The concurrency primitives non-trivially interact among each other, and along with the lightweight thread scheduler, through a cascade of locks and condition variables. Often times, the invariants on which RTS fields can be accessed when are expressed as comments, and enforced through assertions (See [trac:source:rts/Task.h@085c7fe5d4ea6e7b59f944d46ecfeba3755a315b#L37 here] for one such fascinating example). This policy of enforcing through assertions keeps the overheads low, but makes the task of modifying and extending the runtime cumbersome.
 
 But, why would we be interested in modifying GHC's concurrency environment? There are several good reasons to believe that a particular concurrent programming model, or a scheduling policy would not suit every application. With the emergence of many-core processors, we see NUMA effects becoming more prominent, and applications might benefit from NUMA aware scheduling and load balancing policies. Moreover, an application might have a better knowledge of the scheduling requirements -- a thread involved in user-interaction is expected to be given more priority over threads performing background processing. We might want to experiment with various work-stealing or work-sharing policies. More ambitiously, we might choose to build X10 style async-finish or Cilk style spawn-sync task parallel abstractions. Ideally, we would like allow the programmer to write an application that can  seamlessly combine all of these different programming abstractions, with pluggable scheduling and load balancing policies.