XPS: CLCCA: Enhancing the Programmability of Heterogeneous Manycore Systems

随着计算设备被用来解决不断增加的多维数据集的日益复杂和多样化的问题，程序员的任务是编写高性能和节能的代码。为了运行此代码，处理器供应商正在采用不同的系统，其中常规通用内核与图形处理单元(GPU)、加密加速器、数据库加速器和视频编解码器等加速器集成。为了确保这些系统的广泛采用，它们的编程模型必须是有效和易于使用的。不幸的是，这些系统的当前编程模型具有挑战性，需要程序员显式地在内核和加速器之间来回分配、管理和编组内存。因此，软件往往容易出错和错误，并遭受数据复制和移动的管理费用。随着未来系统的异构性程度越来越高，这个问题将会恶化。该建议为内核和加速器开发统一的地址空间，这是有效编程模型的关键部分。统一的地址空间(在虚拟和物理地址中)提高了系统的可编程性，因为：(1)程序员不需要手动分配和管理他们在数百个不同的计算单元之间的数据移动；(2)随着执行转移，系统在不同的组件之间自动分配、复制和迁移数据；(3)这些系统支持需要同时核心和加速器访问公共数据结构的新算法(例如，生产者-消费者程序，其中CPU和GPU通过软件任务队列进行通信)；(4)程序现在跨具有交替存储器层次的系统更可移植。这项工作研究了通过开发新的硬件(例如，新的内存控制器、转换后备缓冲区扩充、击落机制)和操作系统(例如，新的操作系统内存分配机制以及对异类系统和内存上的页面分配、复制和迁移的支持)来支持这些好处(同时保持高性能和低功耗)的机制。

As computing devices are used to solve increasingly complex and diverse problems with ever-increasing multidimensional data-sets, programmers are tasked with writing high-performance and energy-efficient code. To run this code, processor vendors are adopting heterogeneous systems, where conventional general-purpose cores are integrated with accelerators like graphics processing units (GPUs), cryptographic accelerators, database accelerators, and video encoders/decoders. To ensure the widespread adoption of these systems, it is essential that their programming models are effective and easy to use. Unfortunately, current programming models for these systems are challenging, requiring the programmer to explicitly allocate, manage, and marshal memory back and forth between cores and accelerators. As a result, software is often error-prone and buggy, and suffers overheads from data replication and movement. As future systems incorporate increasing levels of heterogeneity, this problem will worsen.This proposal develops unified address spaces for cores and accelerators, which is a key part of an effective programming model. A unified address space (in both virtual and physical addresses) increases system programmability because: (1) programmers need not manually allocate and manage their data movement between hundreds of heterogeneous compute units; (2) the system automatically allocates, replicates, and migrates data among heterogeneous components as execution shifts; (3) these systems support new algorithms that require simultaneous core and accelerator access to common data structures (e.g., producer-consumer programs where CPUs and GPUs communicate through software task queues); (4) programs are now more portable across systems with alternate memory hierarchies. This work studies mechanisms to support these benefits (while maintaining high performance and low power) by developing novel hardware (e.g., new memory controllers, Translation Lookaside Buffer augmentations, shootdown mechanisms) and operating system (OS) support (e.g., new OS memory allocation mechanisms and support for page allocation, replication, and migration on heterogeneous systems and memory).