...
首页> 外文期刊>ETRI journal >Exploiting Thread-Level Parallelism in Lockstep Execution by Partially Duplicating a Single Pipeline
【24h】

Exploiting Thread-Level Parallelism in Lockstep Execution by Partially Duplicating a Single Pipeline

机译:通过部分复制一条管道来利用锁步执行中的线程级并行性

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

In most parallel loops of embedded applications, every iteration executes the exact same sequence of instructions while manipulating different data. This fact motivates a new compiler-hardware orchestrated execution framework in which all parallel threads share one fetch unit and one decode unit but have their own execution, memory, and write-back units. This resource sharing enables parallel threads to execute in lockstep with minimal hardware extension and compiler support Our proposed architecture, called multithreaded lockstep execution processor (MLEP), is a compromise between the single-instruction multiple-data (SIMD) and symmetric multithreading/chip multiprocessor (SMT/CMP) solutions. The proposed approach is more favorable than a typical SIMD execution in terms of degree of parallelism, range of applicability, and code generation, and can save more power and chip area than the SMT/CMP approach without significant performance degradation. For the architecture verification, we extend a commercial 32-bit embedded core AE32000C and synthesize it on Xilinx FPGA. Compared to the original architecture, our approach is 13.5% faster with a 2-way MLEP and 33.7% faster with a 4-way MLEP in EEMBC benchmarks which are automatically parallelized by the Intel compiler.
机译:在嵌入式应用程序的大多数并行循环中,每次迭代都执行完全相同的指令序列,同时处理不同的数据。这一事实激发了一种新的编译器-硬件协调执行框架,其中所有并行线程共享一个获取单元和一个解码单元,但具有自己的执行,内存和回写单元。这种资源共享使并行线程能够以最少的硬件扩展和编译器支持在锁步中执行。我们提出的架构称为多线程锁步执行处理器(MLEP),是单指令多数据(SIMD)与对称多线程/芯片多处理器之间的折衷方案(SMT / CMP)解决方案。就并行度,适用范围和代码生成而言,所建议的方法比典型的SIMD执行更有利,并且与SMT / CMP方法相比,可以节省更多的功率和芯片面积,而不会显着降低性能。为了进行架构验证,我们扩展了商用32位嵌入式内核AE32000C并在Xilinx FPGA上进行了合成。与原始体系结构相比,在EEMBC基准测试中,采用英特尔®编译器自动并行化的2路MLEP和4路MLEP的处理速度分别提高了13.5%和33.7%。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号