Simplified vector-thread architectures for flexible and efficient data-parallel accelerators

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 165-170).This thesis explores a new approach to building data-parallel accelerators that is based on simplifying the instruction set, microarchitecture, and programming methodology for a vector-thread architecture. The thesis begins by categorizing regular and irregular data-level parallelism (DLP), before presenting several architectural design patterns for data-parallel accelerators including the multiple-instruction multiple-data (MIMD) pattern, the vector single-instruction multiple-data (vector-SIMD) pattern, the single-instruction multiple-thread (SIMT) pattern, and the vector-thread (VT) pattern. Our recently proposed VT pattern includes many control threads that each manage their own array of microthreads. The control thread uses vector memory instructions to efficiently move data and vector fetch instructions to broadcast scalar instructions to all microthreads. These vector mechanisms are complemented by the ability for each microthread to direct its own control flow. In this thesis, I introduce various techniques for building simplified instances of the VT pattern. I propose unifying the VT control-thread and microthread scalar instruction sets to simplify the microarchitecture and programming methodology. I propose a new single-lane VT microarchitecture based on minimal changes to the vector-SIMD pattern.(cont.) Single-lane cores are simpler to implement than multi-lane cores and can achieve similar energy efficiency. This new microarchitecture uses control processor embedding to mitigate the area overhead of single-lane cores, and uses vector fragments to more efficiently handle both regular and irregular DLP as compared to previous VT architectures. I also propose an explicitly data-parallel VT programming methodology that is based on a slightly modified scalar compiler. This methodology is easier to use than assembly programming, yet simpler to implement than an automatically vectorizing compiler. To evaluate these ideas, we have begun implementing the Maven data-parallel accelerator. This thesis compares a simplified Maven VT core to MIMD, vector-SIMD, and SIMT cores. We have implemented these cores with an ASIC methodology, and I use the resulting gate-level models to evaluate the area, performance, and energy of several compiled microbenchmarks. This work is the first detailed quantitative comparison of the VT pattern to other patterns. My results suggest that future data-parallel accelerators based on simplified VT architectures should be able to combine the energy efficiency of vector-SIMD accelerators with the flexibility of MIMD accelerators.by Christopher Francis Batten.Ph.D

Hybrid Nanophotonic NOC Design for GPGPU

Due to the massive computational power, Graphics Processing Units (GPUs) have become a popular platform for executing general purpose parallel applications. The majority of on-chip communications in GPU architecture occur between memory controllers and compute cores, thus memory controllers become hot spots and bottle neck when conventional mesh interconnection networks are used. Leveraging this observation, we reduce the network latency and improve throughput by providing a nanophotonic ring network which connects all memory controllers. This new interconnection network employs a new routing algorithm that combines Dimension Ordered Routing (DOR) and nanophotonic ring algorithms. By exploring this new topology, we can achieve to reduce interconnection network latency by 17% on average (up to 32%) and improve IPC by 5% on average (up to 11.5%). We also analyze application characteristics of six CUDA benchmarks on the GPGPU-Sim simulator to obtain better perspective for designing high performance GPU interconnection network

Efficient Precise Dynamic Data Race Detection For Cpu And Gpu

Data races are notorious bugs. They introduce non-determinism in programs behavior, complicate programs semantics, making it challenging to debug parallel programs. To make parallel programming easier, efficient data race detection has been a research topic in the last decades. However, existing data race detectors either sacrifice precision or incur high overhead, limiting their application to real-world applications and scenarios. This dissertation proposes approaches to improve the performance of dynamic data race detection without undermining precision, by identifying and removing metadata redundancy dynamically. This dissertation also explores ways to make it practical to detect data races dynamically for GPU programs, which has a disparate programming and execution model from CPU workloads. Further, this dissertation shows how the structured synchronization model in GPU programs can simplify the algorithm design of data race detection for GPU, and how the unique patterns in GPU workloads enable an efficient implementation of the algorithm, yielding a high-performance dynamic data race detector for GPU programs

Vectorization system for unstructured codes with a Data-parallel Compiler IR

With Dennard Scaling coming to an end, Single Instruction Multiple Data (SIMD) offers itself as a way to improve the compute throughput of CPUs. One fundamental technique in SIMD code generators is the vectorization of data-parallel code regions. This has applications in outer-loop vectorization, whole-function vectorization and vectorization of explicitly data-parallel languages. This thesis makes contributions to the reliable vectorization of data-parallel code regions with unstructured, reducible control flow. Reducibility is the case in practice where all control-flow loops have exactly one entry point. We present P-LLVM, a novel, full-featured, intermediate representation for vectorizers that provides a semantics for the code region at every stage of the vectorization pipeline. Partial control-flow linearization is a novel partial if-conversion scheme, an essential technique to vectorize divergent control flow. Different to prior techniques, partial linearization has linear running time, does not insert additional branches or blocks and gives proved guarantees on the control flow retained. Divergence of control induces value divergence at join points in the control-flow graph (CFG). We present a novel control-divergence analysis for directed acyclic graphs with optimal running time and prove that it is correct and precise under common static assumptions. We extend this technique to obtain a quadratic-time, control-divergence analysis for arbitrary reducible CFGs. For this analysis, we show on a range of realistic examples how earlier approaches are either less precise or incorrect. We present a feature-complete divergence analysis for P-LLVM programs. The analysis is the first to analyze stack-allocated objects in an unstructured control setting. Finally, we generalize single-dimensional vectorization of outer loops to multi-dimensional tensorization of loop nests. SIMD targets benefit from tensorization through more opportunities for re-use of loaded values and more efficient memory access behavior. The techniques were implemented in the Region Vectorizer (RV) for vectorization and TensorRV for loop-nest tensorization. Our evaluation validates that the general-purpose RV vectorization system matches the performance of more specialized approaches. RV performs on par with the ISPC compiler, which only supports its structured domain-specific language, on a range of tree traversal codes with complex control flow. RV is able to outperform the loop vectorizers of state-of-the-art compilers, as we show for the SPEC2017 nab_s benchmark and the XSBench proxy application.Mit dem Ausreizen des Dennard Scalings erreichen die gewohnten Zuwächse in der skalaren Rechenleistung zusehends ihr Ende. Moderne Prozessoren setzen verstärkt auf parallele Berechnung, um den Rechendurchsatz zu erhöhen. Hierbei spielen SIMD Instruktionen (Single Instruction Multiple Data), die eine Operation gleichzeitig auf mehrere Eingaben anwenden, eine zentrale Rolle. Eine fundamentale Technik, um SIMD Programmcode zu erzeugen, ist der Einsatz datenparalleler Vektorisierung. Diese unterliegt populären Verfahren, wie der Vektorisierung äußerer Schleifen, der Vektorisierung gesamter Funktionen bis hin zu explizit datenparallelen Programmiersprachen. Der Beitrag der vorliegenden Arbeit besteht darin, ein zuverlässiges Vektorisierungssystem für datenparallelen Code mit reduziblem Steuerfluss zu entwickeln. Diese Anforderung ist für alle Steuerflussgraphen erfüllt, deren Schleifen nur einen Eingang haben, was in der Praxis der Fall ist. Wir präsentieren P-LLVM, eine ausdrucksstarke Zwischendarstellung für Vektorisierer, welche dem Programm in jedem Stadium der Transformation von datenparallelem Code zu SIMD Code eine definierte Semantik verleiht. Partielle Steuerfluss-Linearisierung ist ein neuer Algorithmus zur If-Conversion, welcher Sprünge erhalten kann. Anders als existierende Verfahren hat Partielle Linearisierung eine lineare Laufzeit und fügt keine neuen Sprünge oder Blöcke ein. Wir zeigen Kriterien, unter denen der Algorithmus Steuerfluss erhält, und beweisen diese. Steuerflussdivergenz induziert Divergenz an Punkten zusammenfließenden Steuerflusses. Wir stellen eine neue Steuerflussdivergenzanalyse für azyklische Graphen mit optimaler Laufzeit vor und beweisen deren Korrektheit und Präzision. Wir verallgemeinern die Technik zu einem Algorithmus mit quadratischer Laufzeit für beliebiege, reduzible Steuerflussgraphen. Eine Studie auf realistischen Beispielgraphen zeigt, dass vergleichbare Techniken entweder weniger präsize sind oder falsche Ergebnisse liefern. Ebenfalls präsentieren wir eine Divergenzanalyse für P-LLVM Programme. Diese Analyse ist die erste Divergenzanalyse, welche Divergenz in stapelallokierten Objekten unter unstrukturiertem Steuerfluss analysiert. Schließlich generalisieren wir die eindimensionale Vektorisierung von äußeren Schleifen zur multidimensionalen Tensorisierung von Schleifennestern. Tensorisierung eröffnet für SIMD Prozessoren mehr Möglichkeiten, bereits geladene Werte wiederzuverwenden und das Speicherzugriffsverhalten des Programms zu optimieren, als dies mit Vektorisierung der Fall ist. Die vorgestellten Techniken wurden in den Region Vectorizer (RV) für Vektorisierung und TensorRV für die Tensorisierung von Schleifennestern implementiert. Wir zeigen auf einer Reihe von steuerflusslastigen Programmen für die Traversierung von Baumdatenstrukturen, dass RV das gleiche Niveau erreicht wie der ISPC Compiler, welcher nur seine strukturierte Eingabesprache verarbeiten kann. RV kann schnellere SIMD-Programme erzeugen als die Schleifenvektorisierer in aktuellen Industriecompilern. Dies demonstrieren wir mit dem nab_s benchmark aus der SPEC2017 Benchmarksuite und der XSBench Proxy-Anwendung