Cliff Click discusses RAIN, H2O, JMM, Parallel Computation, Fork/Joins in the context of performing big data analysis on tons of commodity hardware.
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.