Vishkin’s counterpoint to Dally [1] talks about what the model of computation should now be in light of multicore processors (and their programming difficulties) and parallel processing. His view recommends a new model of computation, other than RAM and PRAM, that considers the communication time to communicate with global memory in addition to the processing time in the central processing unit (CPU).

This counterpoint still favors RAM as the best model--it is simple; it considers all computations sequential; it is mathematically sound as it is based on mathematical induction. Furthermore, multicore programming has a dearth of programmers because programming such systems is simply too difficult. The current exploitation of parallelism supported by the new model for general-purpose application performance falls far behind the single-core CPU that supports the RAM model, which played a key role in easy programming for serial computing and naturally paved the path to PRAM models. By allowing us to reason about the true costs of computation, the parallel explicit communication model (PECM) will allow us to design more efficient computations. However, when PECM is compared with PRAM, the cost-effectiveness appeal for programmers will again tilt the scales to PRAM; the PECM reliance on specialized efficiencies will make the systems even more brittle.

Another fact: not much software is explicitly parallel processing on multicores; even software written on earlier multicores needs to be retuned to run on new multicores. The original rationale of PRAM was to execute an unlimited number of serial operations of RAM in a single step, but remain as simple as RAM.

Overall, the counterpoint view makes a strong case. It favors the RAM and PRAM models against any newer models--at least for the time being, until some universal model appears that can take care of all the energy complexity types.