Search results
Results from the WOW.Com Content Network
Parallel computers can be roughly classified according to the level at which the hardware supports parallelism, with multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters, MPPs, and grids use multiple computers to work on the same task. Specialized parallel computer architectures ...
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 ...
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.
Furthermore, multiprocessor and multithreaded computer systems may be said to exhibit MLP and ILP due to parallelism—but not intra-thread, single process, ILP and MLP. Often, however, we restrict the terms MLP and ILP to refer to extracting such parallelism from what appears to be non-parallel single threaded code.
In computing, interleaving of data refers to the interspersing of fields or channels of different meaning sequentially in memory, in processor registers, or in file formats. For example, for coordinate data, x0 y0 z0 w0 x1 y1 z1 w1 x2 y2 z2 w2 x0 x1 x2 x3 y0 y1 y2 y3 z0 z1 z2 z3 w0 w1 w2 w3. the former is interleaved while the latter is not.
Atanasoff–Berry computer, the first computer with parallel processing [1] Instruction-level parallelism (ILP) is the parallel or simultaneous execution of a sequence of instructions in a computer program. More specifically, ILP refers to the average number of instructions run per step of this parallel execution. [2]: 5
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.
[6] Parallelism vs concurrency; Multi-threading and multi-processing (shared system resources) Synchronization (coordinating access to shared resources) Coordination (managing interactions between concurrent tasks) Concurrency Control (ensuring data consistency and integrity) Inter-process Communication (IPC, facilitating information exchange)