Shared memory processors programming

In working through process control examples, we found that many calculations, data checks, rate checks and other computationally intensive tasks are done at the first level of inference. Considerations of computational efficiency led to a design utilizing two parallel processors with a shared memory (Figure 1). One of the processors is a 68010 programmed in C code. This processor performs computationally intensive, low level tasks which are directed by the expert system in the LISP processor. 

For MIMD shared-memory environments, the evaluation facility is likely to be based on per-processor events and states that are explicitly defined by the programmer and/or automatically defined by the programming environment. There is wide variation in the level of detail that can be obtained by automatic instrumentation. Information may be provided at the level of proce-... 

The shared-memory programming model on the KSR allows a straightforward port of large applications to at least a single processor. KSR claims that an optimized port of AMBER took only three days. KSR has two full-time computational chemists in-house with other systems development staff who were trained as computational chemists. 

The last line causes all processors in the processor set mpi.wor ld coinin to send their partial results to processor 0, where they will be placed in a row named a in the correct order. One can imagine that in many programs the sending and receiving of data from other processors can quickly become complicated. Furthermore, in contrast with the shared-memory program, we have to alter the program with respect to the nonparallel version to obtain the desired result. 

Parallel programming for shared memory computers is relatively straightforward, as all processors can access any location in the common memory. Processor access to the global memory is coordinated by a switch. The complexity of this switch increases rapidly with the number of processors, resulting in longer memory access times, and shared memory machines therefore usually consist of a small number of processors, typically less than 100. In distributed memory computers, there is, in principle, no such bottleneck, as communication networks are constructed in such a way that communication between one pair of processors is typically independent of communication between another pair. 

Each process can have multiple threads of execution. Threads provide independent execution contexts that share the same virtual memory. Nodes with multiple processors can schedule different threads and processes to execute concurrently. Sharing of data between processes is discussed in chapter 3, and an overview of programming with multiple threads is given in chapter 4. 

MIMD systems aUow several processes to share a common set of processors and resources, as shown in Fig. 19.6(b). Multiple processors are joined together in a cooperating environment to execute programs. TypicaUy, one process executes on each processor at a time. The difficulties with traditional MIMD architectures He in fuUy utiUzing the resources when instruction streams staU (due to data dependencies, control dependencies, synchronization problems, memory accesses, or I/O accesses) or in assigning new processes quickly once the current process has finished execution. An important problem with this structure is that... 

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