Mohammad Rezaei discusses fine-grained parallelism along with an algorithm called Aggregation and a concurrent map built to help dealing with it.
Rich Hickey discuses Reducers, a library for dealing with collections that are faster than Clojure’s standard lazy ones and providing support for parallelism.
Cyril Zeller introduces NVIDIA CUDA development, showing how to write and execute C programs on the GPU, how to manage GPU memory and communication with the CPU.
Trisha Gee introduces Disruptor, a concurrency framework based on a data structure – a ring buffer – that enables fast message passing in a parallel environment.
Steve Vinoski believes that actor-oriented languages such as Erlang are better prepared for the challenges of the future: cloud, multicore, high availability and fault tolerance.
Joshua Bloch, Robert Bocchino, Sebastian Burckhardt, Hassan Chafi, Russ Cox, Benedict Gaster, Guy Steele, David Ungar, and Tucker Taft discuss the future of computing in a multicore world.
Danny Coward talks on how Oracle intends to maintain Java in the front line by investing in two features that are trendy today: support for multiple JVM languages and parallel programming.
Stuart Halloway discusses how we use a total control time model, proposing a different one that represents the world more accurately helping to solve some of the concurrency and parallelism problems.
Dale Schumacher explains the actor concept and how it helps us build a computational model resembling the reality around us more accurately than the object-oriented model.
Ralph Johnson presents several data parallelism patterns, including related Java, C# and C++ libraries from Intel and Microsoft, comparing it with other forms of parallelism such as actor programming.
Ralph Johnson presents several data parallelism patterns, including related libraries from Intel and Microsoft, comparing it with other forms of parallel programming such as actor programming.