Processors are required to handle multiple tasks that are either dependent or totally independent. The internal state of such processors usually consists of registers that might hold different values at each particular instant of program execution. At each instant of program execution, the internal state image is called the architecture state of the processor.
When code execution is switched to run another function (e.g., another thread, process or program), then the state of the machine/processor has to be saved so that the new function can utilize the internal registers to build its new state. Once the new function is terminated then its state can be discarded and the state of the previous context will be restored and execution resumes. Such a switch process is called a context switch and usually includes 10's or hundreds of cycles especially with modern architectures that employ large number of registers (e.g., 64, 128, 256) and/or out of order execution.
In thread-aware hardware architectures, it is normal for the hardware to support multiple context states for a limited number of hardware-supported threads. In this case, the hardware duplicates all architecture state elements for each supported thread. This eliminates the need for context switch when executing a new thread. However, this still has multiple draw backs, namely the area, power and complexity of duplicating all architecture state elements (i.e., registers) for each additional thread supported in hardware. In addition, if the number of software threads exceeds the number of explicitly supported hardware threads, then the context switch must still be performed.
This becomes common as parallelism is needed on a fine granularity basis requiring a large number of threads. The hardware thread-aware architectures with duplicate context-state hardware storage do not help non-threaded software code and only reduces the number of context switches for software that is threaded. However, those threads are usually constructed for coarse grain parallelism, and result in heavy software overhead for initiating and synchronizing, leaving fine grain parallelism, such as function calls and loops parallel execution, without efficient threading initiations/auto generation. Such described overheads are accompanied with the difficulty of auto parallelization of such codes using state of the art compiler or user parallelization techniques for non-explicitly/easily parallelized/threaded software codes.