Simultaneous multithreading (SMT) is a technique for improving the overall efficiency of superscalar CPUs with hardware multithreading. SMT permits multiple independent threads of execution to better use the resources provided by modern processor architectures. Details The term multithreading is ambiguous, because not only can multiple threads be executed simultaneously on one CPU core, but also multiple tasks (with different page tables, different task state segments, different protection rings, different I/O permissions, etc.). Although running on the same core, they are completely separated from each other. Multithreading is similar in concept to preemptive multitasking but is implemented at the thread level of execution in modern superscalar processors. Simultaneous multithreading (SMT) is one of the two main implementations of multithreading, the other form being temporal multithreading (also known as super-threading). In temporal multithreading, only one thread of instructions can execute in any given pipeline stage at a time. In simultaneous multithreading, instructions from more than one thread can be executed in any given pipeline stage at a time. This is done without great changes to the basic processor architecture: the main additions needed are the ability to fetch instructions from multiple threads in a cycle, and a larger register file to hold data from multiple threads. The number of concurrent threads is decided by the chip designers. Two concurrent threads per CPU core are common, but some processors support up to eight concurrent threads per core. Because it inevitably increases conflict on shared resources, measuring or agreeing on its effectiveness can be difficult. However, measured energy efficiency of SMT with parallel native and managed workloads on historical 130 nm to 32 nm Intel SMT (hyper-threading) implementations found that in 45 nm and 32 nm implementations, SMT is extremely energy efficient, even with in-order Atom processors. In modern systems, SMT effectively exploits concurrency with very little additional dynamic power. That is, even when performance gains are minimal the power consumption savings can be considerable. Some researchers have shown that the extra threads can be used proactively to seed a shared resource like a cache, to improve the performance of another single thread, and claim this shows that SMT does not only increase efficiency. Others use SMT to provide redundant computation, for some level of error detection and recovery. However, in most current cases, SMT is about hiding memory latency, increasing efficiency, and increasing throughput of computations per amount of hardware used. Taxonomy In