Performance Characterisation and Simulation of Intel's Integrated GPU Architecture

Gera, Prasun; Kim, Hyojong; Hong, Sunpyo; George, Vinod Mohan; Luk, Chi-Keung

doi:10.1109/ispass.2018.00027

Cited by 14 publications

(4 citation statements)

References 16 publications

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“…A full-system CPU/GPU simulation framework sharing some features with our simulator has been presented in [33], however their GPU model is a simplified and generic approximation. A microarchitectural simulator for Intel's integrated GPU has recently been described in [37], which relies on binary instrumentation of kernels for trace generation. While this allows for inspection of GPU code, insertion of tracing code modifies the GPU kernels and interferes with their execution.…”

Section: Related Workmentioning

confidence: 99%

Full-System Simulation of Mobile CPU/GPU Platforms

Kaszyk

Wagstaff

Spink

et al. 2019

2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)

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Graphics Processing Units (GPUs) critically rely on a complex system software stack comprising kernel-and userspace drivers and Just-in-time (JIT) compilers. Yet, existing GPU simulators typically abstract away details of the software stack and GPU instruction set. Partly, this is because GPU vendors rarely release sufficient information about their latest GPU products. However, this is also due to the lack of an integrated CPU/GPU simulation framework, which is complete and powerful enough to drive the complex GPU software environment. This has led to a situation where research on GPU architectures and compilers is largely based on outdated or greatly simplified architectures and software stacks, undermining the validity of the generated results. In this paper we develop a full-system system simulation environment for a mobile platform, which enables users to run a complete and unmodified software stack for a state-of-the-art mobile Arm CPU and Mali-G71 GPU powered device. We validate our simulator against a hardware implementation and Arm's stand-alone GPU simulator, achieving 100% architectural accuracy across all available toolchains. We demonstrate the capability of our GPU simulation framework by optimizing an advanced Computer Vision application using simulated statistics unavailable with other simulation approaches or physical GPU implementations. We demonstrate that performance optimizations for desktop GPUs trigger bottlenecks on mobile GPUs, and show the importance of efficient memory use.

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Section: Related Workmentioning

confidence: 99%

Full-System Simulation of Mobile CPU/GPU Platforms

Kaszyk

Wagstaff

Spink

et al. 2019

2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)

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“…In [1], the authors presented the architectures of Skylake ® and Kabylake ® integrated GPUs, and characterized the performance of the two GPUs through a collection of microbenchmarks. Although it is the first work that takes a detailed look at Intel ® integrated GPU architectures, the paper does not provide the insight of floating-point performance with respect to arithmetic intensity.…”

Section: Related Workmentioning

confidence: 99%

“…Another class of GPUs, with a central processing unit (CPU) and a GPU integrated on the same chip, is commonly used in laptops, desktop computers, and low-power servers. While they are not designed to outperform discrete GPUs due to the power, area, and thermal constrains [1], there is a need to better understand the performance of a processor with an integrated GPU for floating-point intensive applications. The study helps us better understand the characteristics of hardware and software development tools, and the benefit of offloading such applications to an integrated GPU.…”

Section: Introductionmentioning

confidence: 99%

A Study of Geodesic Distance Kernel on an Integrated GPU

Jin

2019

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“…However, The percentage of undisclosed characteristics beyond what GPU vendors have documented is small. Hence, researches have proposed different micro-benchmarks written in programming languages, such as CUDA [9] or OpenCL [10] to understand the hidden characteristics of the hardware for almost every GPU generations/architectures [11]- [15]. Similarly, there are several works to develop assembly toolchains that can provide direct access to the hardware using real machine-dependent opcodes [16]- [19].…”

Section: Introductionmentioning

confidence: 99%

Low Overhead Instruction Latency Characterization for NVIDIA GPGPUs

Arafa

Badawy

Chennupati

et al. 2019

2019 IEEE High Performance Extreme Computing Conference (HPEC)

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The last decade has seen a shift in the computer systems industry where heterogeneous computing has become prevalent. Graphics Processing Units (GPUs) are now present in supercomputers to mobile phones and tablets. GPUs are used for graphics operations as well as general-purpose computing (GPGPUs) to boost the performance of compute-intensive applications. However, the percentage of undisclosed characteristics beyond what vendors provide is not small. In this paper, we introduce a very low overhead and portable analysis for exposing the latency of each instruction executing in the GPU pipeline(s) and the access overhead of the various memory hierarchies found in GPUs at the micro-architecture level. Furthermore, we show the impact of the various optimizations the CUDA compiler can perform over the various latencies. We perform our evaluation on seven different high-end NVIDIA GPUs from five different generations/architectures: Kepler, Maxwell, Pascal, Volta, and Turing. The results in this paper can help architects to have an accurate characterization of the latencies of these GPUs, which will help in modeling the hardware accurately. Also, software developers can perform informed optimizations to their applications.

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Performance Characterisation and Simulation of Intel's Integrated GPU Architecture

Cited by 14 publications

References 16 publications

Full-System Simulation of Mobile CPU/GPU Platforms

Full-System Simulation of Mobile CPU/GPU Platforms

A Study of Geodesic Distance Kernel on an Integrated GPU

Low Overhead Instruction Latency Characterization for NVIDIA GPGPUs

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