Pipeline (architecture des processeurs)

330px|droite|Plan d'un pipeline générique à trois étapes En microarchitecture, un pipeline (ou chaîne de traitement), est l'élément d'un processeur dans lequel l'exécution des instructions est découpée en plusieurs étapes. Le premier ordinateur à utiliser cette technique est l'IBM Stretch, conçu en 1961. Avec un pipeline, le processeur peut commencer à exécuter une nouvelle instruction sans attendre que la précédente soit terminée. Chacune des étapes d’un pipeline est appelé étage. Le nombre d'étages d'un pipeline est appelé sa profondeur. Analogie Le pipeline est un concept s'inspirant du fonctionnement d'une ligne de montage. Considérons que l'assemblage d'un véhicule se compose de trois étapes (avec éventuellement des étapes intermédiaires) : 1. Installation du moteur. 2. Installation du capot. 3. Pose des pneus. Un véhicule dans cette ligne de montage ne peut se trouver que dans une seule position à la fois. Une fois le moteur installé, le véhicule Y continue pour une

Easy and Accurate Hardware-based Program Performance Monitoring

Andrzej Pawel Nowak

The performance monitoring of computer systems is a complex affair, made even more challenging by the increasing gap between hardware and software. Methods that collect and feed data to performance analysis can usually be classified into one of two groups. Methods in the first group (e.g., software instrumentation), can be accurate but have unacceptable overheads and offer no insight into the interaction of the software and the hardware. Methods in the second group (e.g., hardware supported) run in real time and connect code to the response in the architecture, at a considerable decrease in accuracy, with a high degree of specificity to the hardware and therefore with limited actionable information made available. In this work, we focus on the latter group - the collection and analysis of raw performance data sourced from hardware Performance Monitoring Units, built into the majority of processors. The periodic collection of samples (Event Based Sampling), containing code locations, for instance, can be triggered on events such as retired instructions or cache misses. We quantify accuracy improvements to existing methods, propose a better performing method of performance data collection and a new architecture-agnostic method of performance data analysis. In our experiments, we analyze compiled, large-scale production workloads, industry standard benchmarks, and micro-benchmarks reproducing specific architectural behaviors. First, we focus on performance data collection methods and their accuracy, when establishing instruction retirement rates. These vary from least to most advanced and we examine the low level factors influencing accuracy. In particular, we employ a method based on Last Branch Records, a hardware facility present on some Intel architectures, which allows for the sampling of short records of last taken branches leading up to the sample. We compare the most commonly used method, its improved derivatives and the Last Branch Record based method. With respect to the first method, we observe accuracy improvements of up to 18x on synthetic benchmarks and 15x on real applications. Second, to further improve accuracy, we propose a new collection method named Hybrid Basic Block Profiling, fusing those based on Last Branch Records and the widely used Event Based Sampling. We apply simple machine learning techniques to determine the operating criteria. As a demonstration of capability, our method and a profiling tool are used to generate Instruction Mixes, which traditionally require good quality data at the most detailed level of granularity - basic blocks. Compared to software instrumentation, we observe an improvement in runtime of up to 76x, while keeping instruction attribution errors at 2.1% on average. In our third contribution we describe an architecture-agnostic performance analysis methodology called Hierarchical Cycle Accounting. The user is presented with a navigable hierarchy of microarchitectural issues, expressed in a metric that is universal and simple to compare - core cycles. We develop a reference implementation for the Intel Ivy Bridge microarchitecture and provide pointers for implementations on other processor architectures. Our approach and tool are successfully used by non-experts to improve and vectorize complex scientific code. Experts benefit as well - by being able to pinpoint software issues undetected by other methods, and identifying a bug in a family of Intel processors.

EPFL2017

Processeur à haut degré de parallélisme basé sur des composantes sérielles

Ralph Hoffmann

Nowaday, the world of processors is still dominated by the RISC architectures, which foundations have been laid down in the 70's. The RISC concept may be summarized by one word : simplicity. With this concept, much simpler architectures are born, in particular from their instruction sets point of view. As a result, these architectures have a more efficient instruction decoding and smaller computing units which in turn provides the ability to have more of these units for the same price, a reduced clock per instruction ratio — a single cycle per instruction ideally — and increased clock frequencies. As time goes by, RISC architectures have shown outstanding performances, on one hand with the spectacular improvements of semiconductor technologies, and on the other hand with the use of large cache memories and with the development of new ways of execution such as speculative execution. Later developments have greatly altered the original spirit of the simplicity of the RISC concept. Current high performance processors are extremely complex and difficult to design. Moreover, semiconductor technologies begin to show serious problems ignored or neglected for a long time. In this thesis, we would like to introduce a new architecture. Two complementary aspects have been taken in consideration : the design of the processor's units (arithmetic operators, control logic, and so on) and the architectural model, the way the processing units are used. The first aspect has been addressed with an unusual and original approach which involves the use of the serial arithmetic principles. Serial arithmetic tends to reduce the hardware cost, enables higher clock rates, and shows interesting properties which will be developped later on. The research of an architectural model is driven by the same wish of simplicity, recurrent in this field, and will inevitably shift the complexity from the processor to the compiler. This simplification relies on two things : first, the way we think of the problem is inspired by the EPIC philosophy (Explicitly Parallel Instruction Computer), the latest offspring of the VLIW model which advocates the delegation of decisions from the hardware to the software. In one sentence, "the compiler decides, the hardware executes". Second, the way we act has been given by the TTA model (Transport Triggered Architecture), the last offspring of the MOVE model which not only removes from the processor the responsability to decide which processing unit to be used for each instruction (it's already the case in a VLIW processor), but also releases the processor from scheduling the data flows. In doing so, the processor is confined to provide a set of computing units, storage units and means of communication, and the compiler decides how to move the data. In this way, a part of the control logic is removed, communication resources may be fine-tuned and the compiler's code-optimization can be enhanced. With the combination of those elements, we wish to get an architecture which exhibits a better computing power/consumption ratio, which is more flexible, easier to developp and to evolve.

EPFL2004

Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans

Ainaz Eftekhar, Amir Roshan Zamir, Alexander Evan Sax

This paper introduces a pipeline to parametrically sample and render static multi-task vision datasets from comprehensive 3D scans from the real-world. In addition to enabling interesting lines of research, we show the tooling and generated data suffice to train robust vision models. Familiar architectures trained on a generated starter dataset reached state-of-the-art performance on multiple common vision tasks and benchmarks, despite having seen no benchmark or non-pipeline data. The depth estimation network outperforms MiDaS and the surface normal estimation network is the first to achieve human-level performance for in-the-wild surface normal estimation-at least according to one metric on the OASIS benchmark. The Dockerized pipeline with CLI, the (mostly python) code, PyTorch dataloaders for the generated data, the generated starter dataset, download scripts and other utilities are all available through our project website.

IEEE2021