Scientific computing and efficient memory usage

In summary, the conversation discusses the need for efficient data structures and memory management in scientific computing, specifically in the context of parallel programming and working with large arrays. The importance of ram memory throughput and the role of CPU cache management are also mentioned, with a reference to an article on merge sorting and locality of reference.
  • #1
torquil
649
2
Hi!

I wonder if it is possible to get some book recommendations on the subject of efficient data structures and memory management in scientific computing? I do a bit of parallel programming in C/C++ with MPI, and I'm now considering trying my hand at some GPU programming as well.

When working with large arrays there is the possibility of introducing memory bottlenecks, so I would like to learn a bit more about efficient data structures and memory management in the context of scientific computing.

Best regards
Torquil
 
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  • #2
The main issue is the fact that ram memory throughput is greater when it's sequentially accessed or nearly so. How the cpu handles cache is another factor to consider.

There's a brief mention of this in a wiki article on merge sorting, not much detail, but it does contain links to other articles, such as locality of reference.

http://en.wikipedia.org/wiki/Merge_sort#Optimizing_merge_sort
 

Related to Scientific computing and efficient memory usage

1. What is scientific computing?

Scientific computing is the use of computers to solve complex scientific problems through mathematical and computational models. It involves the development of algorithms and software that can efficiently process and analyze large amounts of data.

2. Why is efficient memory usage important in scientific computing?

Efficient memory usage is crucial in scientific computing because it allows for faster processing of data and reduces the overall computational time. This is especially important when dealing with large datasets and complex simulations, where inefficient memory usage can significantly slow down the process.

3. What techniques are used for efficient memory usage in scientific computing?

There are several techniques used for efficient memory usage in scientific computing, such as data compression, parallel computing, and memory hierarchy optimization. These techniques aim to minimize the amount of memory needed to store and process data, as well as to optimize the usage of available memory resources.

4. How can scientific computing help in different fields of research?

Scientific computing has a wide range of applications in various fields of research, such as physics, biology, chemistry, and engineering. It enables researchers to perform complex simulations, analyze large datasets, and visualize data in a more efficient and accurate manner. This can lead to new discoveries, advancements, and innovations in different fields.

5. What are the challenges of scientific computing and efficient memory usage?

One of the main challenges of scientific computing and efficient memory usage is the constant increase in the amount of data that needs to be processed and analyzed. This requires constantly evolving techniques and technologies to handle larger and more complex datasets. Another challenge is balancing the trade-off between memory usage and processing speed, as reducing memory usage can sometimes result in slower computational time.

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