Tag Archives: memory management

Memory Management of .NET List vs. PTL List64 (Video)

We continue our Parallel Template Library (PTL) video series with our “Memory Management in PTL Containers” video. The PTL Containers component for .NET and Java offers powerful data containers that have been optimized for large data sets and for parallelism to take advantage of multicore processors.

In this video, we demonstrate how PTL’s superior container architecture results in better memory management and increased performance. We compare a .NET List to a PTL List64 container. We add from 100 million to 1.1 billion elements to both containers to show how PTL uses memory much more efficiently. We also retrieve all elements from both containers to prove that random access performance was not sacrificed to implement PTL’s superior memory management scheme.

We executed our demo application on a system with one Intel Core i7 processor with 4 physical cores (8 logical cores with hyperthreading) and 32 GB of memory. We also restarted the demo application after each test to run each test independently and get a fair comparison.

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Parallel Template Library (PTL) for .NET and Java Overview (Video)

We’ve been working on a new video series to showcase our Parallel Template Library (PTL), which is a generic and parallel programming library for .NET and Java. Our first video gives an overview of PTL’s strengths and innovations:

Parallel Template Library (PTL) simplifies parallel performance for .NET and Java developers. We developed PTL for software developers who don’t have the time or resources to become parallel programming experts but who still want to take advantage of parallelism and multicore processors to improve the performance of their existing applications. We understand that not everyone will be as passionate about parallel programming as we are, but we strongly believe that it should be used more often to take advantage of modern hardware.

Often, expert software developers focus only on obtaining more performance, and parallel programming solutions are still too complex for the majority of developers. PTL achieves high performance parallelism while still being generic and easy to use.

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