Before revision:  However, they are also often inherently parallel, making them prime targets for modern massively parallel central processing unit (CPU) architectures. While the use of multi-core and distributed computing is widely applied in the Bayesian community,  very little attention has been given to fine-grain parallelisation using single instruction multiple data (SIMD) operations that are available on most modern commodity CPUs . Rather, most fine-grain tuning in the literature has centred around general purpose graphics processing units (GPGPUs).
After revision:  However, they are also often inherently parallel, making them prime targets for modern massively parallel processors. Multi-core and distributed computing is widely applied in the Bayesian community, very little attention has been given to fine-grain parallelisation using single instruction multiple data (SIMD) operations that are available on most modern commodity CPUs . Rather, most fine-grain tuning in the literature has centred around general purpose graphics processing units (GPGPUs).