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Interleaved, preemptive, fine-grained or time-sliced multithreading are more modern terminology. In addition to the hardware costs discussed in the block type of multithreading, interleaved multithreading has an additional cost of each pipeline stage tracking the thread ID of the instruction it is processing.
Fine-grained (or interleaved) The main processor pipeline may contain multiple threads, with context switches effectively occurring between pipe stages (e.g., in the barrel processor). This form of multithreading can be more expensive than the coarse-grained forms because execution resources that span multiple pipe stages may have to deal with ...
the former is interleaved while the latter is not. A processor may support permute instructions, or strided load and store instructions, for moving between interleaved and non-interleaved representations. Interleaving has performance implications for cache coherency, ease of leveraging SIMD hardware, and leveraging a computer's addressing modes.
The Concurrency Representation Theorem in the actor model provides a fairly general way to represent concurrent systems that are closed in the sense that they do not receive communications from outside. (Other concurrency systems, e.g., process calculi can be modeled in the actor model using a two-phase commit protocol. [13])
An initial version of this model was introduced, under the MapReduce name, in a 2010 paper by Howard Karloff, Siddharth Suri, and Sergei Vassilvitskii. [2] As they and others showed, it is possible to simulate algorithms for other models of parallel computation, including the bulk synchronous parallel model and the parallel RAM, in the massively parallel communication model.
In computing, interleaved memory is a design which compensates for the relatively slow speed of dynamic random-access memory (DRAM) or core memory, by spreading memory addresses evenly across memory banks. That way, contiguous memory reads and writes use each memory bank in turn, resulting in higher memory throughput due to reduced waiting for ...
Concurrent computations may be executed in parallel, [3] [6] for example, by assigning each process to a separate processor or processor core, or distributing a computation across a network. The exact timing of when tasks in a concurrent system are executed depends on the scheduling , and tasks need not always be executed concurrently.
MIPS MT provides for both heavyweight virtual processing elements and lighter-weight hardware microthreads. RMI, a Cupertino-based startup, is the first MIPS vendor to provide a processor SOC based on eight cores, each of which runs four threads. The threads can be run in fine-grain mode where a different thread can be executed each cycle.