Blogging Tips: parallel processing

Showing posts with label parallel processing. Show all posts

Spark: Low latency, massively parallel processing framework

While Hadoop fits well in most batch processing workload, and is the primary choice of big data processing today, it is not optimized for ot...

Since the emerging of Hadoop implementation, I have been trying to morph existing algorithms from various areas into the map/reduce model. ...

Hadoop is getting more popular these days. Lets look at what it is good at and what not. The Map/Reduce Programming model Map/Reduce offers...

In my previous post, I talk about the methodology of transforming a sequential algorithm into parallel. After that, we can implement the pa...

Under the category of "Concurrent Oriented Programming", Erlang has got some good attention recently due to some declared success ...

In my previous post , I introduce Map/Reduce model as a powerful model for parallelism . However, although Map/Reduce is simple, powerful a...

We explore the Map/Reduce approach to turn sequential algorithm into parallel Map/Reduce Overview Since the "reduce" operation ne...

The growth of a single CPU has been limited by physical factors such as clock rate, generated heat, power ... etc. Current trend is moving ...