Behind the Scenes: Memory Analysis of Graphical Workloads on Tile-Based GPUs

Ceballos, Germán; Sembrant, Andreas; Carlson, Trevor E.; Black-Schaffer, David

doi:10.1109/ispass.2018.00009

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Research has also been done into code motion techniques to optimize power consumption rather than performance [42]. Other papers explore offline compiler optimizations for GLSL shaders [7], and analysing memory usage patterns [8], but little other work has examined the data redundancy and code specialization opportunities of real-world graphics workloads.…”

Section: Related Workmentioning

confidence: 99%

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Specialization Opportunities in Graphical Workloads

Crawford

O’Boyle

2019

2019 28th International Conference on Parallel Architectures and Compilation Techniques (PACT)

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Computer games are complex performance-critical graphical applications which require specialized GPU hardware. For this reason, GPU drivers often include many heuristics to help optimize throughput. Recently however, new APIs are emerging which sacrifice many heuristics for lower-level hardware control and more predictable driver behavior. This shifts the burden for many optimizations from GPU driver developers to game programmers, but also provides numerous opportunities to exploit application-specific knowledge. This paper examines different opportunities for specializing GPU code and reducing redundant data transfers. Static analysis of commercial games shows that 5-18% of GPU code is specializable by pruning dead data elements or moving portions to different graphics pipeline stages. In some games, up to 97% of the programs' data inputs of a particular type, namely uniform variables, are unused, as well as up to 62% of those in the GPU internal vertex-fragment interface. This shows potential for improving memory usage and communication overheads. In some test scenarios, removing dead uniform data can lead to 6x performance improvements. We also explore the upper limits of specialization if all dynamic inputs are constant at run-time. For instance, if uniform inputs are constant, up to 44% of instructions can be eliminated in some games, with a further 14% becoming constant-foldable at compile time. Analysis of run-time traces, reveals that 48-91% of uniform inputs are constant in real games, so values close to the upper limit may be achieved in practice.

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Section: Related Workmentioning

confidence: 99%

“…As well as having more time to perform complex shader optimizations, game developers also have access to more context. It has been shown that over 99% rendering data is reused between frames [8], but is impractically large to keep in cache. Uniform variables are one type of data required by shaders.…”

Section: Introductionmentioning

confidence: 99%

Specialization Opportunities in Graphical Workloads

Crawford

O’Boyle

2019

2019 28th International Conference on Parallel Architectures and Compilation Techniques (PACT)

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“…GPU simulation is central to driving GPU research and development. 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].…”

Section: Introductionmentioning

confidence: 99%

“…Maximum error of a performance metric reported in the original publication 3. Original publication does not provide an accuracy evaluation against a hardware implementation of the simulated GPU.…”

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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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Optimization strategies for GPUs: an overview of architectural approaches

Masola

Capodieci

2023

International Journal of Parallel, Emergent and Distributed Sys

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Behind the Scenes: Memory Analysis of Graphical Workloads on Tile-Based GPUs

Cited by 5 publications

References 15 publications

Specialization Opportunities in Graphical Workloads

Specialization Opportunities in Graphical Workloads

Full-System Simulation of Mobile CPU/GPU Platforms

Optimization strategies for GPUs: an overview of architectural approaches

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