Shared Memory vs. Message Passing: Which is Better for Concurrent Read-Only Access to Large Data Structures?

Shared Memory vs. Message Passing for Large Data Structures

Concurrency models such as message passing, employed by Go and Erlang, eliminate complex locks by avoiding shared state. However, in the case of multiple clients desiring concurrent read-only access to a massive data structure like a suffix array, the following questions arise:

1. Shared State vs. Message Passing: Performance and Memory Consumption

Would shared memory surpass message passing in terms of speed and memory usage due to the absence of locks and the presence of only one copy of the data?

Answer: The BEAM implementation in Erlang does not necessitate data copying in message passing. Instead, it employs a reference-based system for large data structures. This approach reduces memory consumption compared to shared memory.

2. Message Passing Approach

In a message passing context, several options exist:

• Single Data Holder Process: One process exclusively holds the data structure, and clients request data sequentially.
• Chunked Data: The data structure is divided into chunks, and multiple processes hold different chunks.

The optimal approach depends on the specific data structure and workload requirements.

3. Hardware Considerations

Can modern CPU and memory architectures eliminate bottlenecks between shared memory and message passing implementations?

Answer: Modern CPUs support parallel memory access, reducing performance penalties for shared memory. However, message passing can offer advantages in scenarios where data locality is crucial or multiple processes access the data concurrently with varying workloads.

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