MIMD / MPMD并行性(MIMD/MPMD parallelism)

假设群集中的一组计算机正在使用全局共享数据块。

如果它是分布式内存模型，则说整个结构被发送到集群中的每个节点。

集群中的每个节点并行地对共享数据块的各部分执行不同的操作。

我的问题是：它是否可以被识别为多指令多数据（MIMD）模型，因为整个数据块由集群中的每个节点共享/转移到集群中的每个节点？

Assume that there is a globally shared data block that is being used by a set of machines in a cluster.

If it is a distributed memory model, say the whole structure is sent to every node in the cluster.

Each node in the cluster performs different operations to the parts of the shared data block in parallel.

My question is: can it be identified as a Multiple Instruction, Multiple Data (MIMD) model since the whole data block is shared by/transferred to every node in the cluster?

最满意答案

如果对不同的数据应用相同的操作，那么您就具有数据级并行性，根据Flynn的Taxonomy，它等同于SIMD （单指令，多数据流），通常应用于GPU处理。

多指令多数据（ MIMD ）需要不同的处理器可以在不同的数据上执行不同的指令。

所以根据定义，你的问题的答案是肯定的。

If you apply the same operation to different pieces of data then you have data-level parallelism, which according to Flynn's Taxonomy is equivalent to SIMD (Single Instruction, Multiple Data streams), typically applied in GPU processing.

Multiple Instruction, Multiple Data (MIMD) entail that different processors may be executing different instructions on different data.

So according to the definitions, the answer to your question is yes.

MIMD / MPMD并行性(MIMD/MPMD parallelism)

假设群集中的一组计算机正在使用全局共享数据块。

如果它是分布式内存模型，则说整个结构被发送到集群中的每个节点。

集群中的每个节点并行地对共享数据块的各部分执行不同的操作。

我的问题是：它是否可以被识别为多指令多数据（MIMD）模型，因为整个数据块由集群中的每个节点共享/转移到集群中的每个节点？

Assume that there is a globally shared data block that is being used by a set of machines in a cluster.

If it is a distributed memory model, say the whole structure is sent to every node in the cluster.

Each node in the cluster performs different operations to the parts of the shared data block in parallel.

My question is: can it be identified as a Multiple Instruction, Multiple Data (MIMD) model since the whole data block is shared by/transferred to every node in the cluster?

最满意答案

如果对不同的数据应用相同的操作，那么您就具有数据级并行性，根据Flynn的Taxonomy，它等同于SIMD （单指令，多数据流），通常应用于GPU处理。

多指令多数据（ MIMD ）需要不同的处理器可以在不同的数据上执行不同的指令。

所以根据定义，你的问题的答案是肯定的。

If you apply the same operation to different pieces of data then you have data-level parallelism, which according to Flynn's Taxonomy is equivalent to SIMD (Single Instruction, Multiple Data streams), typically applied in GPU processing.

Multiple Instruction, Multiple Data (MIMD) entail that different processors may be executing different instructions on different data.

So according to the definitions, the answer to your question is yes.