Multi2Sim Kepler: A detailed architectural GPU simulator

Gong, Xun; Ubal, Rafael; Kaeli, David

doi:10.1109/ispass.2017.7975298

Cited by 23 publications

(9 citation statements)

References 10 publications

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“…We model our baseline GPU architecture using the Mult2Sim simulator [5,30], which is a cyclelevel heterogeneous simulation framework supporting both GPU and CPU simulation. All experiments are based on a baseline GPU model similar to the AMD Radeon 7970, whose hardware specifications are summarized in Table 2.…”

Section: Evaluation 61 Methodologymentioning

confidence: 99%

Haws

Gong

et al. 2019

ACM Trans. Archit. Code Optim.

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Graphics Processing Units (GPUs) have become an attractive platform for accelerating challenging applications on a range of platforms, from High Performance Computing (HPC) to full-featured smartphones. They can overcome computational barriers in a wide range of data-parallel kernels. GPUs hide pipeline stalls and memory latency by utilizing efficient thread preemption. But given the demands on the memory hierarchy due to the growth in the number of computing cores on-chip, it has become increasingly difficult to hide all of these stalls.In this article, we propose a novel Hint-Assisted Wavefront Scheduler (HAWS) to bypass long-latency stalls. HAWS starts by enhancing a compiler infrastructure to identify potential opportunities that can bypass memory stalls. HAWS includes a wavefront scheduler that can continue to execute instructions in the shadow of a memory stall, executing instructions speculatively, guided by compiler-generated hints. HAWS increases utilization of GPU resources by aggressively fetching/executing speculatively. Based on our simulation results on the AMD Southern Islands GPU architecture, at an estimated cost of 0.4% total chip area, HAWS can improve application performance by 14.6% on average for memory intensive applications.

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Section: Evaluation 61 Methodologymentioning

confidence: 99%

Haws

Gong

et al. 2019

ACM Trans. Archit. Code Optim.

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“…To simulate ARGA, we use a modified version of Multi2sim [31], a cycle accurate CPU-GPU simulator. The kernel code is modified to implement the proposed design and enable runtime simulation.…”

Section: Methodsmentioning

confidence: 99%

Arga

Peroni

Imani

Nejatollahi

et al. 2019

Proceedings of the 56th Annual Design Automation Conference 2019

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Many data-driven applications including computer vision, speech recognition, and medical diagnostics show tolerance to error during computation. These applications are often accelerated on GPUs, but high computational costs limit performance and increase energy usage. In this paper, we present ARGA, an approximate computing technique capable of accelerating GPGPU applications. ARGA provides an approximate lookup table to GPGPU cores to avoid recomputing instructions with identical or similar values. We propose multi-table parallel lookup which enables computational reuse to significantly speed-up GPGPU computation by checking incoming instructions in parallel. The inputs of each operation are searched for in a lookup table. Matches resulting in an exact or low error are removed from the floating point pipeline and used directly as output. Matches producing highly inaccurate results are computed on exact hardware to minimize application error. We simulate our design by placing ARGA within each core of an Nvidia Kepler Architecture Titan and an AMD Southern Island 7970. We show our design improves performance throughput by up to 2.7× and improves EDP by 5.3× for 6 GPGPU applications while maintaining less than 5% output error. We also show ARGA accelerates inference of a LeNet NN by 2.1× and improves EDP by 3.7× without significantly impacting classification accuracy. CCS CONCEPTS • Computer systems organization → Multicore architectures; • Computing methodologies → Machine learning approaches.

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“…In order to further motivate our full-system approach to CPU/GPU simulation, we initially review the most popular GPU simulators: GPGPU-SIM [16] and MULTI2SIM [10], [11]. In Fig.…”

Section: B State-of-the-artmentioning

confidence: 99%

“…It is used for early design space exploration and architecture tuning [1]- [3], evaluation of GPU compilation techniques [4], application development and optimization [5], and in virtual platforms for system software development [6]. While Central Processing Unit (CPU) simulation techniques have reached maturity, GPU simulation often suffers from the following problems: (a) instruction sets are not accurately modeled, but approximated by an artificial, low-level intermediate representation [7], [8], (b) GPU simulators do not model existing commercial GPUs, but only simplified GPU architectures [9], (c) instead of using vendor provided driver stacks and compilers, GPU simulators often rely on simplified system software, which may behave entirely differently to original tools [10], [11], and (d) GPUs are treated as standalone devices, not modeling any CPU-GPU transactions [12]. This has led researchers using GPU simulation to rely on tools providing questionable accuracy [13].…”

Section: Introductionmentioning

confidence: 99%

“…This has led researchers using GPU simulation to rely on tools providing questionable accuracy [13]. For example, many existing GPU simulators including gem5-GPU [14], GpuTejas [15], Multi2Sim [10], GPGPU-Sim [16], and Multi2Sim-Kepler [11] claim cycle-accuracy. However, despite their claimed cycle-accuracy all of these simulators show significant differences in the reported cycle count (or other reported performance metrics) compared to actual hardware.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Multi2Sim Kepler: A detailed architectural GPU simulator

Cited by 23 publications

References 10 publications

Haws

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Arga

Full-System Simulation of Mobile CPU/GPU Platforms

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