- Becareful of Lockfree algorithms - 1 Update
- Neural networks everywhere - 1 Update
| Sky89 <Sky89@sky68.com>: May 01 07:52PM -0400 Hello.. Becareful of Lockfree algorithms Look at how they are "much" slower than lock based algorithms, read the following from acmqueue to notice it: == In practice, however, lock-free algorithms may not live up to these performance expectations. Consider, for example, Michael and Scott's lock-free queue algorithm.11 This algorithm implements a queue using a linked list, with items enqueued to the tail and removed from the head using CAS loops. (The exact details are not as important as the basic idea, which is similar in spirit to the example in figure 4.) Despite this, as figure 6a shows, the lock-free algorithm fails to scale beyond four threads and eventually performs worse than the two-lock queue algorithm. The reason for this poor performance is CAS failure: as the amount of concurrency increases, so does the chance that a conflicting CAS gets interleaved in the middle of a core's read-compute-update CAS region, causing its CAS to fail. CAS operations that fail in this way pile useless work on the critical path. Although these failing CASes do not modify memory, executing them still requires obtaining exclusive access to the variable's cache line. This delays the time at which later operations obtain the cache line and complete successfully (see figure 5b, in which only two operations complete in the same time that three operations completed in figure 5a). Read more here: https://queue.acm.org/detail.cfm?id=2991130 == Thank you, Amine Moulay Ramdane. |
| Sky89 <Sky89@sky68.com>: May 01 02:55PM -0400 Hello, Read this: Neural networks everywhere New chip reduces neural networks' power consumption by up to 95 percent, making them practical for battery-powered devices. MIT researchers have developed a special-purpose chip that increases the speed of neural-network computations by three to seven times over its predecessors, while reducing power consumption 93 to 96 percent. That could make it practical to run neural networks locally on smartphones or even to embed them in household appliances. Read more here: https://www.sciencedaily.com/releases/2018/02/180214145854.htm Thank you, Amine Moulay Ramdane. |
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