CUDA Unified Memory를 위한 데이터 관리 및 프리페칭 기법

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2022. 8. 이재진.Unified Memory (UM) is a component of CUDA programming model which provides a memory pool that has a single address space and can be accessed by both the host and the GPU. When UM is used, a CUDA program does not need to explicitly move data between the host and the device. It also allows GPU memory oversubscription by using CPU memory as a backing store. UM significantly lessens the burden of a programmer and provides great programmability. However, using UM solely does not guarantee good performance. To fully exploit UM and improve performance, the programmer needs to add user hints to the source code to prefetch pages that are going to be accessed during the CUDA kernel execution. In this thesis, we propose three frameworks that exploits UM to improve the ease-of-programming while maximizing the application performance. The first framework is HUM, which hides host-to-device memory copy time of traditional CUDA program without any code modification. It overlaps the host-to-device memory copy with host computation or CUDA kernel computation by exploiting Unified Memory and fault mechanisms. The evaluation result shows that executing the applications under HUM is, on average, 1.21 times faster than executing them under original CUDA. The speedup is comparable to the average speedup 1.22 of the hand-optimized implementations for Unified Memory. The second framework is DeepUM which exploits UM to allow GPU memory oversubscription for deep neural networks. While UM allows memory oversubscription using a page fault mechanism, page fault handling introduces enormous overhead. We use a correlation prefetching technique to solve the problem and hide the overhead. The evaluation result shows that DeepUM achieves comparable performance to the other state-of-the-art approaches. At the same time, our framework can run larger batch size that other methods fail to run. The last framework is SnuRHAC that provides an illusion of a single GPU for the multiple GPUs in a cluster. Under SnuRHAC, a CUDA program designed to use a single GPU can utilize multiple GPUs in a cluster without any source code modification. SnuRHAC automatically distributes workload to multiple GPUs in a cluster and manages data across the nodes. To manage data efficiently, SnuRHAC extends Unified Memory and exploits its page fault mechanism. We also propose two prefetching techniques to fully exploit UM and to maximize performance. The evaluation result shows that while SnuRHAC significantly improves ease-of-programming, it shows scalable performance for the cluster environment depending on the application characteristics.Unified Memory (UM)는 CUDA 프로그래밍 모델에서 제공하는 기능 중 하나로 단일 메모리 주소 공간에 CPU와 GPU가 동시에 접근할 수 있도록 해준다. 이에 따라, UM을 사용할 경우 CUDA 프로그램에서 명시적으로 프로세서간에 데이터를 이동시켜주지 않아도 된다. 또한, CPU 메모리를 backing store로 사용하여 GPU의 메모리 크기 보다 더 많은 양의 메모리를 필요로 하는 프로그램을 실행할 수 있도록 해준다. 결과적으로, UM은 프로그래머의 부담을 크게 덜어주고 쉽게 프로그래밍 할 수 있도록 도와준다. 하지만, UM을 있는 그대로 사용하는 것은 성능 측면에서 좋지 않다. UM은 page fault mechanism을 통해 동작하는데 page fault를 처리하기 위해서는 많은 오버헤드가 발생하기 때문이다. UM을 사용하면서 최대의 성능을 얻기 위해서는 프로그래머가 소스 코드에 여러 힌트나 앞으로 CUDA 커널에서 사용될 메모리 영역에 대한 프리페치 명령을 삽입해주어야 한다. 본 논문은 UM을 사용하면서도 쉬운 프로그래밍과 최대의 성능이라는 두마리 토끼를 동시에 잡기 위한 방법들을 소개한다. 첫째로, HUM은 기존 CUDA 프로그램의 소스 코드를 수정하지 않고 호스트와 디바이스 간에 메모리 전송 시간을 최소화한다. 이를 위해, UM과 fault mechanism을 사용하여 호스트-디바이스 간 메모리 전송을 호스트 계산 혹은 CUDA 커널 실행과 중첩시킨다. 실험 결과를 통해 HUM을 통해 애플리케이션을 실행하는 것이 그렇지 않고 CUDA만을 사용하는 것에 비해 평균 1.21배 빠른 것을 확인하였다. 또한, Unified Memory를 기반으로 프로그래머가 소스 코드를 최적화한 것과 유사한 성능을 내는 것을 확인하였다. 두번째로, DeepUM은 UM을 활용하여 GPU의 메모리 크기 보다 더 많은 양의 메모리를 필요로 하는 딥 러닝 모델을 실행할 수 있게 한다. UM을 통해 GPU 메모리를 초과해서 사용할 경우 CPU와 GPU간에 페이지가 매우 빈번하게 이동하는데, 이때 많은 오버헤드가 발생한다. 두번째 방법에서는 correlation 프리페칭 기법을 통해 이 오버헤드를 최소화한다. 실험 결과를 통해 DeepUM은 기존에 연구된 결과들과 비슷한 성능을 보이면서 더 큰 배치 사이즈 혹은 더 큰 하이퍼파라미터를 사용하는 모델을 실행할 수 있음을 확인하였다. 마지막으로, SnuRHAC은 클러스터에 장착된 여러 GPU를 마치 하나의 통합된 GPU처럼 보여준다. 따라서, 프로그래머는 여러 GPU를 대상으로 프로그래밍 하지 않고 하나의 가상 GPU를 대상으로 프로그래밍하면 클러스터에 장착된 모든 GPU를 활용할 수 있다. 이는 SnuRHAC이 Unified Memory를 클러스터 환경에서 동작하도록 확장하고, 필요한 데이터를 자동으로 GPU간에 전송하고 관리해주기 때문이다. 또한, UM을 사용하면서 발생할 수 있는 오버헤드를 최소화하기 위해 다양한 프리페칭 기법을 소개한다. 실험 결과를 통해 SnuRHAC이 쉽게 GPU 클러스터를 위한 프로그래밍을 할 수 있도록 도와줄 뿐만 아니라, 애플리케이션 특성에 따라 최적의 성능을 낼 수 있음을 보인다.1 Introduction 1 2 Related Work 7 3 CUDA Unified Memory 12 4 Framework for Maximizing the Performance of Traditional CUDA Program 17 4.1 Overall Structure of HUM 17 4.2 Overlapping H2Dmemcpy and Computation 19 4.3 Data Consistency and Correctness 23 4.4 HUM Driver 25 4.5 HUM H2Dmemcpy Mechanism 26 4.6 Parallelizing Memory Copy Commands 29 4.7 Scheduling Memory Copy Commands 31 5 Framework for Running Large-scale DNNs on a Single GPU 33 5.1 Structure of DeepUM 33 5.1.1 DeepUM Runtime 34 5.1.2 DeepUM Driver 35 5.2 Correlation Prefetching for GPU Pages 36 5.2.1 Pair-based Correlation Prefetching 37 5.2.2 Correlation Prefetching in DeepUM 38 5.3 Optimizations for GPU Page Fault Handling 42 5.3.1 Page Pre-eviction 42 5.3.2 Invalidating UM Blocks of Inactive PyTorch Blocks 43 6 Framework for Virtualizing a Single Device Image for a GPU Cluster 45 6.1 Overall Structure of SnuRHAC 45 6.2 Workload Distribution 48 6.3 Cluster Unified Memory 50 6.4 Additional Optimizations 57 6.5 Prefetching 58 6.5.1 Static Prefetching 58 6.5.2 Dynamic Prefetching 61 7 Evaluation 62 7.1 Framework for Maximizing the Performance of Traditional CUDA Program 62 7.1.1 Methodology 63 7.1.2 Results 64 7.2 Framework for Running Large-scale DNNs on a Single GPU 70 7.2.1 Methodology 70 7.2.2 Comparison with Naive UM and IBM LMS 72 7.2.3 Parameters of the UM Block Correlation Table 78 7.2.4 Comparison with TensorFlow-based Approaches 79 7.3 Framework for Virtualizing Single Device Image for a GPU Cluster 81 7.3.1 Methodology 81 7.3.2 Results 84 8 Discussions and Future Work 91 9 Conclusion 93 초록 111박

Aeronautical engineering: A continuing bibliography with indexes (supplement 216)

This bibliography lists 505 reports, articles and other documents introduced into the NASA scientific and technical information system in July, 1987

Nova combinação de hardware e de software para veículos de desporto automóvel baseada no processamento directo de funções gráficas

Doutoramento em Engenharia EletrónicaThe main motivation for the work presented here began with previously conducted experiments with a programming concept at the time named "Macro". These experiments led to the conviction that it would be possible to build a system of engine control from scratch, which could eliminate many of the current problems of engine management systems in a direct and intrinsic way. It was also hoped that it would minimize the full range of software and hardware needed to make a final and fully functional system. Initially, this paper proposes to make a comprehensive survey of the state of the art in the specific area of software and corresponding hardware of automotive tools and automotive ECUs. Problems arising from such software will be identified, and it will be clear that practically all of these problems stem directly or indirectly from the fact that we continue to make comprehensive use of extremely long and complex "tool chains". Similarly, in the hardware, it will be argued that the problems stem from the extreme complexity and inter-dependency inside processor architectures. The conclusions are presented through an extensive list of "pitfalls" which will be thoroughly enumerated, identified and characterized. Solutions will also be proposed for the various current issues and for the implementation of these same solutions. All this final work will be part of a "proof-of-concept" system called "ECU2010". The central element of this system is the before mentioned "Macro" concept, which is an graphical block representing one of many operations required in a automotive system having arithmetic, logic, filtering, integration, multiplexing functions among others. The end result of the proposed work is a single tool, fully integrated, enabling the development and management of the entire system in one simple visual interface. Part of the presented result relies on a hardware platform fully adapted to the software, as well as enabling high flexibility and scalability in addition to using exactly the same technology for ECU, data logger and peripherals alike. Current systems rely on a mostly evolutionary path, only allowing online calibration of parameters, but never the online alteration of their own automotive functionality algorithms. By contrast, the system developed and described in this thesis had the advantage of following a "clean-slate" approach, whereby everything could be rethought globally. In the end, out of all the system characteristics, "LIVE-Prototyping" is the most relevant feature, allowing the adjustment of automotive algorithms (eg. Injection, ignition, lambda control, etc.) 100% online, keeping the engine constantly working, without ever having to stop or reboot to make such changes. This consequently eliminates any "turnaround delay" typically present in current automotive systems, thereby enhancing the efficiency and handling of such systems.A principal motivação para o trabalho que conduziu a esta tese residiu na constatação de que os actuais métodos de modelação de centralinas automóveis conduzem a significativos problemas de desenvolvimento e manutenção. Como resultado dessa constatação, o objectivo deste trabalho centrou-se no desenvolvimento de um conceito de arquitectura que rompe radicalmente com os modelos state-of-the-art e que assenta num conjunto de conceitos que vieram a ser designados de "Macro" e "Celular ECU". Com este modelo pretendeu-se simultaneamente minimizar a panóplia de software e de hardware necessários à obtenção de uma sistema funcional final. Inicialmente, esta tese propõem-se fazer um levantamento exaustivo do estado da arte na área específica do software e correspondente hardware das ferramentas e centralinas automóveis. Os problemas decorrentes de tal software serão identificados e, dessa identificação deverá ficar claro, que praticamente todos esses problemas têm origem directa ou indirecta no facto de se continuar a fazer um uso exaustivo de "tool chains" extremamente compridas e complexas. De forma semelhante, no hardware, os problemas têm origem na extrema complexidade e inter-dependência das arquitecturas dos processadores. As consequências distribuem-se por uma extensa lista de "pitfalls" que também serão exaustivamente enumeradas, identificadas e caracterizadas. São ainda propostas soluções para os diversos problemas actuais e correspondentes implementações dessas mesmas soluções. Todo este trabalho final faz parte de um sistema "proof-of-concept" designado "ECU2010". O elemento central deste sistema é o já referido conceito de “Macro”, que consiste num bloco gráfico que representa uma de muitas operações necessárias num sistema automóvel, como sejam funções aritméticas, lógicas, de filtragem, de integração, de multiplexagem, entre outras. O resultado final do trabalho proposto assenta numa única ferramenta, totalmente integrada que permite o desenvolvimento e gestão de todo o sistema de forma simples numa única interface visual. Parte do resultado apresentado assenta numa plataforma hardware totalmente adaptada ao software, bem como na elevada flexibilidade e escalabilidade, para além de permitir a utilização de exactamente a mesma tecnologia quer para a centralina, como para o datalogger e para os periféricos. Os sistemas actuais assentam num percurso maioritariamente evolutivo, apenas permitindo a calibração online de parâmetros, mas nunca a alteração online dos próprios algoritmos das funcionalidades automóveis. Pelo contrário, o sistema desenvolvido e descrito nesta tese apresenta a vantagem de seguir um "clean-slate approach", pelo que tudo pode ser globalmente repensado. No final e para além de todas as restantes características, o “LIVE-PROTOTYPING” é a funcionalidade mais relevante, ao permitir alterar algoritmos automóveis (ex: injecção, ignição, controlo lambda, etc.) de forma 100% online, mantendo o motor constantemente a trabalhar e sem nunca ter de o parar ou re-arrancar para efectuar tais alterações. Isto elimina consequentemente qualquer "turnaround delay" tipicamente presente em qualquer sistema automóvel actual, aumentando de forma significativa a eficiência global do sistema e da sua utilização

Summary of Research 1994

The views expressed in this report are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government.This report contains 359 summaries of research projects which were carried out under funding of the Naval Postgraduate School Research Program. A list of recent publications is also included which consists of conference presentations and publications, books, contributions to books, published journal papers, and technical reports. The research was conducted in the areas of Aeronautics and Astronautics, Computer Science, Electrical and Computer Engineering, Mathematics, Mechanical Engineering, Meteorology, National Security Affairs, Oceanography, Operations Research, Physics, and Systems Management. This also includes research by the Command, Control and Communications (C3) Academic Group, Electronic Warfare Academic Group, Space Systems Academic Group, and the Undersea Warfare Academic Group