The HPPLS Procedure


Threading refers to the organization of computational work into multiple tasks (processing units that can be scheduled by the operating system). A task is associated with a thread. Multithreading refers to the concurrent execution of threads. When multithreading is possible, substantial performance gains can be realized compared to sequential (single-threaded) execution.

The number of threads that the HPPLS procedure spawns is determined by the number of CPUs on a machine and can be controlled in the following ways:

  • You can specify the CPU count by using the CPUCOUNT= SAS system option. For example, if you specify the following statements, the HPPLS procedure schedules threads as if it executed on a system that has four CPUs, regardless of the actual CPU count.

    options cpucount=4;
  • You can specify the NTHREADS= option in the PERFORMANCE statement to determine the number of threads. This specification overrides the system option. Specify NTHREADS=1 to force single-threaded execution.

The number of threads per machine is displayed in the "Performance Information" table, which is part of the default output. The HPPLS procedure allocates one thread per CPU.

The tasks that the HPPLS procedure multithreads are primarily defined by dividing the data that are processed on a single machine among the threads—that is, the HPPLS procedure implements multithreading through a data-parallel model. For example, if the input data set has 1,000 observations and PROC HPPLS runs on four threads, then 250 observations are associated with each thread. All operations that require access to the data are then multithreaded. These operations include the following:

  • variable levelization

  • effect levelization

  • formation of the crossproducts matrix

  • computation of loadings, weights, scores, generalized inverse, and residual sums of squares

  • scoring of observations

In addition, operations on matrices such as sweeps might be multithreaded if the matrices are of sufficient size to realize performance benefits from managing multiple threads for the particular matrix operation.