Compiler Driven Automatic Kernel Context Migration for Heterogeneous Computing

Gad, Ramy; SuB, Tim; Brinkmann, André

doi:10.1109/icdcs.2014.47

Cited by 4 publications

(4 citation statements)

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“…Even though there are static code analyzers for detecting such pointers at compilation time, there are situations where these tools fail, for example, when the alias depends on runtime conditions [36,37]. Moreover, there exist also tools that instrument the program code and detect at runtime if an array is accessed by using different pointers [38]. But we do not employ any of these tools.…”

Section: Limitationsmentioning

confidence: 99%

Pure Functions in C: A Small Keyword for Automatic Parallelization

Süß

Nagel

Vef

et al. 2020

Int J Parallel Prog

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The need for parallel task execution has been steadily growing in recent years since manufacturers mainly improve processor performance by increasing the number of installed cores instead of scaling the processor's frequency. To make use of this potential, an essential technique to increase the parallelism of a program is to parallelize loops. Several automatic loop nest parallelizers have been developed in the past such as PluTo. The main restriction of these tools is that the loops must be statically analyzable which, among other things, disallows function calls within the loops. In this article, we present a seemingly simple extension to the C programming language which marks functions without side-effects. These functions can then basically be ignored when the automatic parallelizer checks the parallelizability of loops. We integrated the approach into the GCC compiler toolchain and evaluated it by running several real-world applications. Our experiments show that the C extension helps to identify additional parallelization opportunities and, thus, to significantly increase the performance of applications.

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Section: Limitationsmentioning

confidence: 99%

Pure Functions in C: A Small Keyword for Automatic Parallelization

Süß

Nagel

Vef

et al. 2020

Int J Parallel Prog

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Section: Related Workmentioning

confidence: 99%

“…The vast majority of past efforts in heterogeneous computing focus on CPU/GPU systems [28,63] in which a GPU accelerator device is attached to a host CPU [51,66,67,78]. Gad, et al [28] propose a similar context migration process between CPU and GPU. Past efforts have also focused on single-ISA heterogeneous systems [45][46][47] which use cores of the same ISA but with different ISA extensions or micro-architecture (e.g., ARM's big.LITTLE [29], NVidia's Kal-El [67]).…”

Section: Related Workmentioning

confidence: 99%

Cross-ISA execution of SIMD regions for improved performance

Pang

Lyerly

Ravindran

2019

Proceedings of the 12th ACM International Conference on Systems and Storage

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We investigate the effectiveness of executing SIMD workloads on multiprocessors with heterogeneous Instruction Set Architecture (ISA) cores. Heterogeneous ISAs offer an intriguing clock speed/parallelism tradeoff for workloads with frequent usage of SIMD instructions. We consider dynamic migration of SIMD and non-SIMD workloads across ISA-different cores to exploit this trade-off. We present the necessary modifications for a general compiler/run-time infrastructure to transform the dynamic program state of SIMD regions at run-time from one ISA format to another for cross-ISA migration and execution. Additionally, we present a SIMD-aware scheduling policy that makes cross-ISA migration decisions that improve system throughput. We prototype a heterogeneous-ISA system using an Intel Xeon x86-64 server and a Cavium ThunderX ARMv8 server and evaluate the effectiveness of our infrastructure and scheduling policy. Our results reveal that cross-ISA execution migration within SIMD regions can yield throughput gains up to 36% compared to traditional homogeneous ISA systems.

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“…Although there are static code analyzers for detecting such pointers at compilation time, there are situations where these tools fail, e.g., if the alias depends on runtime conditions [16] [28]. Other tools instrument program codes and detect (at runtime) if an array is accessed by using different pointers [12].…”

Section: The Integration Of Pure Functions Into Cmentioning

confidence: 99%