multicore support in C#

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The amount of performance gained by the use of a multicore processor depends on the problem being solved and the algorithms used, as well as their implementation in software (Amdahl's law). For so-called "embarrassingly parallel" problems, a dual-core processor with two cores at 2GHz may perform very nearly as fast as a single core of 4GHz. Other problems though may not yield so much speedup. This all assumes however that the software has been designed to take advantage of available parallelism. If it hasn't, there will not be any speedup at all. However, the processor will multitask better since it can run two programs at once, one on each core.

In addition to operating system (OS) support, adjustments to existing software are required to maximize utilization of the computing resources provided by multi-core processors. Also, the ability of multi-core processors to increase application performance depends on the use of multiple threads within applications. The situation is improving: for example the American PC game developer Valve Corporation has stated that it will use multi core optimizations for the next version of its Source engine, shipped with Half-Life 2: Episode Two, the next installment of its Half-Life series., and Crytek is developing similar technologies for CryEngine 2, which powers their game, Crysis. Emergent Game Technologies' Gamebryo engine includes their Floodgate technology which simplifies multicore development across game platforms. See Dynamic Acceleration Technology for the Santa Rosa platform for an example of a technique to improve single-thread performance on dual-core processors.

Integration of a multi-core chip drives production yields down and they are more difficult to manage thermally than lower-density single-chip designs. Intel has partially countered this first problem by creating its quad-core designs by combining two dual-core on a single die with a unified cache, hence any two working dual-core dies can be used, as opposed to producing four cores on a single die and requiring all four to work to produce a quad-core. From an architectural point of view, ultimately, single CPU designs may make better use of the silicon surface area than multiprocessing cores, so a development commitment to this architecture may carry the risk of obsolescence. Finally, raw processing power is not the only constraint on system performance. Two processing cores sharing the same system bus and memory bandwidth limits the real-world performance advantage. If a single core is close to being memory bandwidth limited, going to dual-core might only give 30% to 70% improvement. If memory bandwidth is not a problem, a 90% improvement can be expected. It would be possible for an application that used two CPUs to end up running faster on one dual-core if communication between the CPUs was the limiting factor, which would count as more than 100% improvement.

Managing concurrency acquires a central role in developing parallel applications. The basic steps in designing parallel applications are:

Partitioning
The partitioning stage of a design is intended to expose opportunities for parallel execution. Hence, the focus is on defining a large number of small tasks in order to yield what is termed a fine-grained decomposition of a problem.

Communication
The tasks generated by a partition are intended to execute concurrently but cannot, in general, execute independently. The computation to be performed in one task will typically require data associated with another task. Data must then be transferred between tasks so as to allow computation to proceed. This information flow is specified in the communication phase of a design.

Agglomeration
In the third stage, we move from the abstract toward the concrete. We revisit decisions made in the partitioning and communication phases with a view to obtaining an algorithm that will execute efficiently on some class of parallel computer. In particular, we consider whether it is useful to combine, or agglomerate, tasks identified by the partitioning phase, so as to provide a smaller number of tasks, each of greater size. We also determine whether it is worthwhile to replicate data and/or computation.

Mapping
In the fourth and final stage of the parallel algorithm design process, we specify where each task is to execute. This mapping problem does not arise on uniprocessors or on shared-memory computers that provide automatic task scheduling.
On the other hand, on the server side, multicore processors are ideal because they allow many users to connect to a site simultaneously and have independent threads of execution. This allows for Web servers and application servers that have much better throughput.