GAML

GAML is a parallel implementation of lazy ML [17] on a shared-memory computer.

• Several G-machines cooperate in reducing a graph concurrently with the intention of speeding up the reduction.
• Annotations in the source program determine partitions to be evaluated in parallel. Annotation consists of `#' (usually, called fork or spark ).
• Automatic parallelization (with information of what the compiler is doing) is possible. Locking and unlocking of tags are used to ensure that when sharing takes place, a graph being run on one G-machine cannot be run by another G-machine. For performance improvement, the G-machines must cooperate. A scheduling of fork pools is necessary.
• Heap management divides the heap space into from-space and to-space pages, a to-space page being allocated by a global manager to each processor initially. A processor that runs out of its heap page can request for more heap to-space page. If this request cannot be fulfilled, there is a page fault, and the processor will enter a collector state. All the other processors will end up in the collector state naturally when they have used up their allocated heap page. Stop and Copy garbage collection is performed after swapping the to-space and from-space pages of the heap. Parallel garbage collection of all running processors is done, but program execution is halted.
• The stacks for unwinding the spine, for intermediate results of the arithmetic computations, and for remembering the G-machine states, do not require constant checks for overflow due to parallel implementation.
• Parallelism is seen to be beneficial. However, as the number of processors is increased, garbage collection time also increases. This is due to more objects to be copied during garbage collection. Furthermore, this also increases the number of stacks and hence the cost due to stack overflow checks.