In the fields of programming and data processing, collective communication is an operation used for exchanging data between multiple processes in parallel computing. Here are common collective communication operations and their meanings:
1. Broadcast#
- Operation: Sends data from one process to all other processes.
- Meaning: One process possesses certain data and needs to distribute that data to all other processes. This is often used to make all processes share a common value.
- Example: In distributed computing, a master process needs to send configuration parameters to all worker processes.
2. Reduce#
- Operation: Aggregates data from multiple processes according to a certain operation (such as summation, maximum, minimum, etc.) and sends the result to a specified process.
- Meaning: Multiple processes each have a portion of data and want to obtain a final result through some merging operation and pass it to the master process.
- Example: Summing the results from each node and then passing the total to the master node.
3. All-Reduce#
- Operation: Similar to reduce, but the final reduction result is sent to all processes.
- Meaning: All processes need to share the final result of the reduction operation, rather than sending it to just one process.
- Example: In deep learning, multiple nodes need to synchronize gradient information to update model parameters.
4. Scatter#
- Operation: Divides data from one process into chunks and sends them to other processes, with each process receiving a portion.
- Meaning: One process has a large amount of data that needs to be distributed to multiple processes for distributed computation.
- Example: In matrix multiplication, distributing a portion of matrix rows to different nodes for parallel computation.
5. Gather#
- Operation: Opposite of scatter, multiple processes each have data, and ultimately they gather it into one process.
- Meaning: After multiple processes have processed their respective portions of data, they need to merge the results into one process.
- Example: After parallel processing, the master process needs to collect results from each subprocess to construct the final result.
6. All-Gather#
- Operation: Each process sends its data to all other processes.
- Meaning: Each process has independent data, and ultimately all processes need to share data from all processes.
- Example: In distributed learning, the local gradients from each node need to be sent to all other nodes to update parameters.
7. All-to-All#
- Operation: Each process sends its data to all other processes, and each process also receives data from all other processes.
- Meaning: All processes need to exchange data with each other, typically used in scenarios requiring global communication.
- Example: In parallel sorting algorithms, nodes need to exchange portions of sorted data for global sorting.
8. Scan#
- Operation: Sequentially accumulates data from multiple processes, resulting in each process receiving the accumulated result of its own data and that of previous processes.
- Meaning: A type of prefix accumulation operation where each process obtains the merged result of data from all previous processes.
- Example: Used for operations such as calculating cumulative sums or cumulative products.
These collective communication operations are very important in distributed computing and parallel algorithms, helping multiple processing units efficiently exchange and synchronize data.