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23 Feb 2015
Instruction-level parallelism (ILP) is a measure of how many of the instructions in a computer program can be executed simultaneously. There are two approaches to instruction level parallelism: Hardware.
Credits. • © John Owens / UC Davis 2007–9. • Thanks to many sources for slide material: Computer. Organization and Design (Patterson & Hennessy) ©. 2005, Computer Architecture (Hennessy & Patterson). © 2007, Inside the Machine (Jon Stokes) © 2007, ©. Dan Connors / University of Colorado 2007, © Wen-.
Instruction-level parallelism (ILP) is a measure of how many operations in a system are simultaneously executable. Instruction pipelining, out-of-order execution, speculative execution, and superscalar architectures enable high instruction-level parallelism in a single core. Multiple processors and multithreading enable high
Instruction pipelining where the execution of multiple instructions can be partially overlapped. Superscalar execution, VLIW, and the closely related Explicitly Parallel Instruction Computing concepts, in which multiple execution units are used to execute multiple instructions in parallel.
25 Apr 2007 •Instruction Level Parallelism (2.1). •Compiler techniques for Exposing ILP (2.2). •Reducing Branch Costs with Prediction (2.3). •Overcoming Data Hazards with Dynamic. Scheduling (2.4). •Dynamic Scheduling: Examples and the. Algorithm (2.5). •Hardware-Based Speculation (2.6). •Exploiting ILP using
ILP is important for executing instructions in parallel and hiding latencies the architecture). • overlaps execution of different instructions. • execute all steps in the execution cycle simultaneously, but on different instructions. Exploits ILP by executing several IBM Stretch (1961): the first general-purpose pipelined computer
Advanced Computer Architecture. Instruction Level Parallelism. Instruction Level Parallelism. Myung Hoon, Sunwoo. School of Electrical and Computer Engineering. Ajou University
Thread level Parallelism (SMT, multi-core computers). Making Computers Think Parallel. Human. Write code yourself, directly controlling each processor. Compiler. Let the compiler convert your sequential code into parallel instructions. Hardware. Implementations of ILP. Pipelining; Superscalar Architecture. Dependency
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