What is the Most Efficient Way to Implement MPI in a Large Vector Algorithm?

[maverick_starstrider]

Hi,

I'm writing an algorithm in MPI which basically splits up a massive vector amongst different nodes (i.e. node 1 has entries 0-1,000,000, node 2 has 1,000,000 - 2,000,000, etc.) and basically my algorithm requires that some calculation be done on each element of the array and the result gets added to another component of a duplicate array of the same size and split up in the same way which, most likely, won't be located on the same node (i.e. after computing on element 1000 on node 1 I find that I have to add the result to element 1,000,102 which is on node 2). So basically, if each node has n components of the vector on it it's going to have to send the result to n different places AND receive lots of potential changes to its own section of the vector. Now my question is, what is the BEST way to do this? I'm torn between:

-after computing each element ISend the result (assuming it needs to be sent to another server) and then do a sweep of IReceives to see if there are any results from other servers that are trying to be sent to me.
-the same thing using buffered send (IBSend). However, memory usage is a huge issue (I'm basically going to make my vector as big as possible) so I don't know how many buffers I need. Should I set up one local buffer for each server (i.e. buffer 2 on server 1 is big enough for a single message and is reserved for IBSend's to server 2). But then what if I need to send to another server but there's still an outgoing message waiting to be received on the other side. I'll have to block until it gets picked up
-The final option I'm wondering about is MPI-2's windowing features and using the put command to simply place the result where it needs to be placed.

Can someone who knows a fair amount about MPI performance considerations help me determine which implementation will be the most effective. Any help is greatly appreciated

 

[lofgran]

.

Hi there,

As a fellow scientist working with MPI, I can understand your dilemma. It's important to consider performance when designing an algorithm for distributed computing. In your case, it seems like a combination of both ISend and IReceive and MPI-2's windowing features could be the most effective approach.

With ISend and IReceive, you can achieve better performance by overlapping communication and computation. This means that while one node is computing, another node can be receiving data, reducing the overall execution time. However, as you mentioned, this approach requires careful management of buffers and can lead to blocking if not done properly.

On the other hand, MPI-2's windowing features can provide a more efficient and flexible solution. By using MPI_Put, you can directly place the result where it needs to be without the need for explicit communication. This can save time and resources, especially if you have a large vector and need to constantly send and receive data between nodes.

Ultimately, the best approach will depend on the specific requirements and constraints of your algorithm. I would suggest experimenting with both approaches and measuring their performance to determine which one works best for your particular case. Also, don't be afraid to consult with other experts in the field or seek out resources and tutorials on MPI performance optimization. Good luck with your algorithm!

 

[quicknote]

.

I would recommend considering the specific needs and constraints of your algorithm before deciding on the best implementation for MPI. Each of the options you mentioned has its own advantages and limitations, and the best approach will depend on factors such as the size and complexity of your vector, the number of nodes and their capabilities, and the communication patterns within your algorithm.

If memory usage is a major concern, then using MPI-2's windowing features and the put command may be a good option. This approach allows for direct data transfer between processes without the need for intermediate buffers, which can save on memory usage. However, it may also require more complex code and may not be the most efficient for all types of communication patterns.

On the other hand, using ISend and IReceive can be more straightforward and efficient for point-to-point communication between nodes. However, as you mentioned, it may require more memory usage and may not be the best approach for more complex communication patterns.

Buffered send (IBSend) can also be a good option if you have a good estimate of the buffer size needed for each node and can manage the potential blocking that may occur. However, if the buffer size is not properly optimized, it can also lead to memory issues.

In general, it's important to carefully consider the trade-offs between memory usage, communication patterns, and code complexity when deciding on the best implementation for MPI. It may also be helpful to consult with other experts in the field or conduct some performance testing to determine the most efficient approach for your specific algorithm.