SHF:Small: Data Triggered Threads for Removing Redundant Execution and Increasing Parallelism

SHF:Small：数据触发线程，用于消除冗余执行并增加并行性

• 批准号: 1219059
• 负责人: Dean Tullsen
• 金额: $ 50万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1219059&HistoricalAwards=false
• 关键词: SHF; Small; Data; Triggered; Threads

As high-performance general-purpose processors advance further into the chip multiprocessor era, with ever increasing core counts, the industry faces two huge challenges. The first is how to effectively use that hardware parallelism when much of the available software does not parallelize easily. The second challenge is managing the power and energy consumed by these processors. Given constrained power, we can only scale performance if we improve the performance delivered per Watt. Data-triggered threads (DTT) is a new programming and execution model designed to address both of these issues. Any conventional architecture that exploits parallelism does so by initiating new parallel computation based on control flow ? that is, execution (i.e., the core?s program counter) reaches a fork instruction or maybe a pthread create call. DTT instead spawns a new thread when data in memory is changed. The programmer specifies a function that is not attached to a set of call sites (a place in the program where the function is called), but rather is attached to a variable or even a field in a data structure type. When that variable is modified by the program, a thread in another core (or multithreading context) is automatically spawned to execute the data-triggered thread, containing code that depends on the changed data.Data-triggered threads provide two key advantages over traditional mechanisms for describing parallelism. The first is that the dependent computation becomes available immediately, as soon as the source data is modified. The second is that the dependent computation need only be executed if the triggering data is actually modified ? in many cases it is not. This work exploits a huge new opportunity to eliminate redundant computation. In the C SPEC benchmarks, 78% of loads are redundant (meaning the same load fetches the same value as the last time it went to the same address). The computation which operates on those values is often also redundant. Researchers in computer architecture and related areas have been working to reduce the power consumed by each instruction, and have made steady progress. It is far preferable, though, to just not execute those instructions ? no power or energy optimization will beat that.This research is exploring a number of opportunities to exploit this new execution model, including: (1) Data triggered threads via architectural support, (2) software-only data triggered threads, (3) data triggered threads to complement existing parallel applications, (4) automatic generation of data triggered threads in the compiler, and (5) programming experience with data triggered threads ? how does DTT code written from scratch differ from programs modified to use DTT, what kind of code will novice programmers produce, and how does that impact the architectural and software runtime implementations.Generation-to-generation performance scaling of processors is critically important to the national and world economy ? not just to hardware vendors, but also the software industries that sell products every time someone upgrades their system. The data triggered threads programming and execution model represents solutions to the two key barriers to performance scaling. It addresses the parallelism crisis by giving the programmer a new way to express parallelism. It addresses the power problem in the most effective way possible ? by not executing computation that does not need to be done.

随着高性能通用处理器进一步进入芯片多处理器时代，随着内核数量的不断增加，该行业面临着两个巨大的挑战。 第一个问题是，当许多可用软件不容易并行化时，如何有效地使用硬件并行性。 第二个挑战是管理这些处理器消耗的功率和能量。 在功率受限的情况下，只有提高每瓦性能，我们才能扩展性能。 数据触发线程（DTT）是一种新的编程和执行模型，旨在解决这两个问题。 任何利用并行性的传统架构都是通过基于控制流启动新的并行计算来实现的。即执行（即，核心？的程序计数器）到达fork指令或者可能是pthread创建调用。当内存中的数据发生更改时，DTT会生成一个新线程。 程序员指定一个函数，该函数不附加到一组调用位置（程序中调用该函数的位置），而是附加到一个变量，甚至是数据结构类型中的一个字段。 当该变量被程序修改时，另一个内核（或多线程上下文中）中的线程会自动生成以执行数据触发线程，其中包含依赖于更改的数据的代码。数据触发线程与传统的描述并行性的机制相比有两个关键优势。 第一个是，一旦源数据被修改，相关计算就立即可用。 第二个是，只有当触发数据实际上被修改时，才需要执行依赖计算。在许多情况下，情况并非如此。 这项工作利用了一个巨大的新机会来消除冗余计算。在C SPEC基准测试中，78%的加载是冗余的（这意味着相同的加载获取的值与上次到达相同地址时相同）。 对这些值进行运算的计算通常也是冗余的。计算机体系结构和相关领域的研究人员一直致力于降低每条指令的功耗，并取得了稳步进展。然而，最好是不执行这些指令。这项研究正在探索利用这种新的执行模式的许多机会，包括：（1）经由体系结构支持的数据触发线程，（2）仅软件数据触发线程，（3）补充现有并行应用的数据触发线程，（4）在编译器中自动生成数据触发线程，（5）有数据触发线程的编程经验？从头开始编写的DTT代码与修改后使用DTT的程序有何不同，新手程序员将生成什么样的代码，以及这对体系结构和软件运行时实现有何影响。处理器的逐代性能扩展对国家和世界经济至关重要？不仅仅是硬件供应商，还有每当有人升级他们的系统时就销售产品的软件行业。数据触发线程编程和执行模型代表了性能扩展的两个关键障碍的解决方案。它解决了并行性危机，为程序员提供了一种新的方式来表达并行性。它以最有效的方式解决了电力问题？通过不执行不需要完成的计算。