Performance Prediction in GPU Architectures: A Comprehensive Analysis

This report analyzes the challenges and solutions in GPU performance prediction, focusing on the complexities of hardware architectures. It presents PPT-GPU, a scalable and accurate simulation framework for predicting the performance of applications on different GPU architectures. The report also discusses the creation of novel GPU predictive models that do not require run-time information, implemented using recurrent neural networks, and explores the prediction of cache performance using reuse distance analysis. The study examines various models, including those based on PTX and deep structured learning, and compares them against real GPU devices and simulators like GPGPU-Sim. The content covers GPU architecture, performance modeling, and the utility of different prediction techniques, with the goal of providing insights into optimizing GPU code and improving the efficiency and accuracy of performance predictions. The report references multiple research papers, including those on PPT-GPU, GPU static modeling using PTX and deep structured learning, and GPU cache performance estimation using reuse distance analysis, along with the GPU Module of a Performance Prediction Toolkit. It evaluates the results, showing the predicted performance of PPT-GPU is within a 10 percent error compared to real devices and is highly scalable, up to 450x faster than GPGPU-Sim.

Running head: GPU MODULE
GPU MODULE
Name of the Student
Name of the university
Author note

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The article suggests that GPU module for performance prediction is an evolved
technology in the sphere of high-performance computing. According to the author, these
GPU based clusters will be among the most powerful super computers of later generations.
Thus based on the present scenario in this sphere the paper provides a GPU Module of the
Performance Prediction Toolkit. The paper consists of three different models of GPU and all
these three models have been validated against three different benchmark applications. In the
section of related work the authors have described the concept in a proper way for the readers
to understand the same (Chapuis, Eidenbenz and Santhi 2016). The section of GPU
architecture provides a proper explanation of the architecture so that the readers can
understand this. Thus the content of the article is relevant to the topic and does justice to the
same. Performance modeling has been depicted as a challenging problem owing to the
complexities in the architecture of the hardware (Arafa et al. 2019). The paper presents PPT-
GPU as both scalable and accurate simulation framework that helps GPU code developers for
predicting the performance of the applications in a faster way. To prove the scope of the
paper, experimental setup has been done and the results have also been provided. The authors
have acknowledged the works that they have cited in the paper and this shows that the
sources are authentic. This focuses on creation of novel GPU predictive models that do not
need of the run-time information (Guerreiro, Ilic, Roma and Tomás 2019). The article makes
comparison of the main scope that it has considered along with the different concepts that
have been developed till date. The subheadings in the article are used aptly to tell a reader
where to find the appropriate content.
Proposing a memory model for predicting the overall cache performance in the GPUs
has become important. The readers not having an idea about GPU will understand the
concept just by reading the introduction of the article which provides the definition. The
purpose as to why this scope should be focused on can be clearly understood from the

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background section which includes GPU architecture and GPU Reuse Distance. The gaming
application have shown a lot of improvements be it in graphics and functionality and GPU
makes the present day gaming applications to run more efficiently. The paper mentions about
various GPU emulators that have been proposed till date (Arafa et al. 2019). These emulators
model the runtime behavior of the GPUs and are made use of to obtain various metrics in
case of a given execution without requiring accessing the GPU. The concept of GPU and its
application in the present times is clear from the articles. The well written part of architecture
of GPU helps in understanding the different elements in the architecture. The future versions
of GPU give an idea what this will be like in days to come and the paper also provides
performance projections by using figures which clarifies the concept even more.

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References
Arafa, Y., Badawy, A.H.A., Chennupati, G., Santhi, N. and Eidenbenz, S., 2019. Ppt-gpu:
Scalable gpu performance modeling. IEEE Computer Architecture Letters, 18(1), pp.55-58.
Arafa, Y., Chennupati, G., Barai, A., Badawy, A.H.A., Santhi, N. and Eidenbenz, S., 2019,
October. GPUs Cache Performance Estimation using Reuse Distance Analysis. In 2019 IEEE
38th International Performance Computing and Communications Conference (IPCCC) (pp.
1-8). IEEE.
Chapuis, G., Eidenbenz, S. and Santhi, N., 2016, January. Gpu performance prediction
through parallel discrete event simulation and common sense. In Proceedings of the 9th EAI
International Conference on Performance Evaluation Methodologies and Tools (pp. 204-
211).
Guerreiro, J., Ilic, A., Roma, N. and Tomás, P., 2019. GPU Static Modeling Using PTX and
Deep Structured Learning. IEEE Access, 7, pp.159150-159161.