Instruction-level parallelism

Résumé

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. Discussion ILP must not be confused with concurrency. In ILP there is a single specific thread of execution of a process. On the other hand, concurrency involves the assignment of multiple threads to a CPU's core in a strict alternation, or in true parallelism if there are enough CPU cores, ideally one core for each runnable thread. There are two approaches to instruction-level parallelism: hardware and software. Hardware level works upon dynamic parallelism, whereas the software level works on static parallelism. Dynamic parallelism means the processor decides at run time which instructions to execute in parallel, whereas static parallelism means the compiler decides which instructions to execute in parallel. The Pentium processor w

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https://en.wikipedia.org/wiki/Instruction-level_parallelism

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Multiprocessors are a core component in all types of computing infrastructure, from phones to datacenters. This course will build on the prerequisites of processor design and concurrency to introduce the essential technologies required to combine multiple processing elements into a single computer.

The course studies techniques to exploit Instruction-Level Parallelism (ILP) statically and dynamically. It also addresses some aspects of the design of domain-specific accelerators. Finally, it explores security challenges based on microarchitectural features and hardware isolation techniques.

The course completes the introduction to computer architecture.

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Instruction-level parallelism for low-power embedded processors

Jean-Michel Puiatti

EPFL2000

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Dynamically Matching ILP Characteristics Via a Heterogeneous Clustered Microarchitecture

Lei Chen

Applications vary in the degree of instruction level parallelism (ILP) available to be exploited by a superscalar processor. The ILP can also vary significantly within an application. On one end of the microarchitecture space are monolithic superscalar designs that exploit parallelism within an application. At another end of the spectrum are clustered architectures having many simple cores that can be clocked at a higher frequency than a comparable monolithic design. A disadvantage of the clustered design is the cost to transmit results between clusters which potentially limits the performance even in high ILP applications if instructions are not mapped carefully to minimize cross communication. In this paper, we propose an approach that incorporates the strengths of the prior two by clustering multiple integer ALUs in an asymmetric fashion. In one cluster, a 6-way out-of-order pipeline efficiently executes code having high ILP. In another cluster, a simpler, but deeper, 2-way pipeline running at twice the frequency speeds up regions of code having little ILP. We use the parallelism metrics gathered from the dynamic Data Dependence Tracking (DDT) mechanism to dynamically steer instruction windows to a cluster. We demonstrate that the heterogeneous cluster design can improve performance by up to 30% over a monolithic 8- way superscalar processor.

2004

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Establishing a base of trust with performance counters for enterprise workloads

Andrzej Pawel Nowak, Willy Zwaenepoel

Understanding the performance of large, complex enterprise-class applications is an important, yet nontrivial task. Methods using hardware performance counters, such as profiling through event-based sampling, are often favored over instrumentation for analyzing such large codes, but rarely provide good accuracy at the instruction level. This work evaluates the accuracy ofmultiple eventbased sampling techniques and quantifies the impact of a range of improvements suggested in recent years. The evaluation is performed on instances of three modern CPU architectures, using designated kernels and full applications. We conclude that precisely distributed events considerably improve accuracy, with further improvements possible when using Last Branch Records. We also present practical recommendations for hardware architects, tool developers and performance engineers, aimed at improving the quality of results.

2015

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