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1. Closing the Gap: Improving the Accuracy of gem5’s GPU Models

   Vishnu Ramadas; Daniel Kouchekinia; Ndubuisi Osuji; Matthew D. Sinclair (June 2023, 5th gem5 Users' Workshop)

   In recent years, we have been enhancing and updating gem5’s GPU support, including enhanced gem5’s GPU support to enable running ML workloads. Moreover, we created, validated, and released a Docker image with the proper software and libraries needed to run AMD’s GCN3 and Vega GPU models in gem5. With this container, users can run the gem5 GPU model, as well as build the ROCm applications that they want to run in the GPU model, out of the box without needing to properly install the appropriate ROCm software and libraries. Additionally, we updated gem5 to make it easier to reproduce results, including releasing support for a number of GPU workloads in gem5-resources and enabling continuous integration testing for a variety of GPU workloads. Current gem5 support focuses on Carrizo- and Vega-class GPUs. Unfortunately, these models do not always provide high accuracy relative to the equivalent ”real” GPUs. This leads to a mismatch in expectations: when prototyping new optimizations in gem5 users may draw the wrong conclusions about the efficacy of proposed optimizations if gem5’s GPU models do not provide high fidelity. Accordingly, to help bridge this divide, we design a series of micro-benchmarks designed expose the latencies, bandwidths, and sizes of a variety of GPU components on real GPUs. By iteratively applying fixes and improvements to gem’s GPU model, we significantly improve its fidelity relative to real AMD GPUs. 

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2. Improving the Speed of gem5’s GPU Regression Tests

   James Braun; Matthew D. Sinclair (June 2023, 5th gem5 Users' Workshop)

   n recent years, we have been enhancing and updating gem5’s GPU support. First, we have enhanced gem5’s GPU support for ML workloads such that gem5 can now run. Moreover, as part of this support, we created, validated, and released a Docker image that contains the proper software and libraries needed to run GCN3 and Vega GPU models in gem5. With this container, users can run the gem5 GPU model, as well as build the ROCm applications that they want to run in the GPU model, out of the box without needing to properly install the appropriate ROCm software and libraries. Additionally, we have updated gem5 to make it easier to reproduce results, including releasing support for a number of GPU workloads in gem5-resources and enabling continuous integration testing on future GPU commits. However, in an effort to provide sufficient coverage, the cur- rent testing support for GPU tests requires significant runtime both for the nightly and weekly regression tests. Currently most of these regression tests test the GPU SE mode support, since GPU FS mode support is still nascent. Unfortunately, much of this time is spent parsing input files to create arrays and other data structures that the GPU subsequently computes on. Although SE mode does not simulate the system calls needed to read these input files, nevertheless this still represents a significant overhead that increases runtime and prevents other tests (potentially providing additional coverage) from being run in that same timeframe. In an effort to address this, in the work we have been working on utilizing SE mode’s avoiding modeling system calls to speed up the runtime of the GPU regression tests. Specifically, we redesign the input reading phase of these GPU tests to create and use mmap’d files for their input arrays (which SE mode completes all at once) instead of reading in the files entry by entry. In doing so, we see significant reductions in runtime of at least 29% 

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3. Improving the Speed of gem5’s GPU Regression Tests

   James Braun; Matthew D. Sinclair (June 2023, 5th gem5 Users' Workshop)

   In recent years, we have been enhancing and updating gem5’s GPU support. First, we have enhanced gem5’s GPU support for ML workloads such that gem5 can now run. Moreover, as part of this support, we created, validated, and released a Docker image that contains the proper software and libraries needed to run GCN3 and Vega GPU models in gem5. With this container, users can run the gem5 GPU model, as well as build the ROCm applications that they want to run in the GPU model, out of the box without needing to properly install the appropriate ROCm software and libraries. Additionally, we have updated gem5 to make it easier to reproduce results, including releasing support for a number of GPU workloads in gem5-resources and enabling continuous integration testing on future GPU commits. However, in an effort to provide sufficient coverage, the cur- rent testing support for GPU tests requires significant runtime both for the nightly and weekly regression tests. Currently most of these regression tests test the GPU SE mode support, since GPU FS mode support is still nascent. Unfortunately, much of this time is spent parsing input files to create arrays and other data structures that the GPU subsequently computes on. Although SE mode does not simulate the system calls needed to read these input files, nevertheless this still represents a significant overhead that increases runtime and prevents other tests (potentially providing additional coverage) from being run in that same timeframe. In an effort to address this, in the work we have been working on utilizing SE mode’s avoiding modeling system calls to speed up the runtime of the GPU regression tests. Specifically, we redesign the input reading phase of these GPU tests to create and use mmap’d files for their input arrays (which SE mode completes all at once) instead of reading in the files entry by entry. In doing so, we see significant reductions in runtime of at least 29% 

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4. Modeling Modern GPU Applications in gem5

   Roarty, Kyle; Sinclair, Matthew D. (May 2020, gem5 Users Workshop)

   In 2018, AMD added support for an updated gem5 GPU model based on their GCN3 architecture. Having a high-fidelity GPU model allows for more accurate research into optimizing modern GPU applications. However, the complexity of getting the necessary libraries and drivers, needed for this model to run GPU applications in gem5, made it difficult to use. This post describes the work we have done with increasing the usability of the GPU model by simplifying the setup process, extending the types of applications that can be run, and optimizing parts of the software stack used by the GPU model. 

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5. Further Closing the GAP: Improving the Accuracy of gem5's GPU Models

   Ramadas, Vishnu; Kouchekinia, Daniel; Sinclair, Matthew D (April 2024, 6th Young Architects' (YArch) Workshop)

   The breakdown in Moore’s Law and Dennard Scaling is leading to drastic changes in the makeup and constitution of computing systems. For example, a single chip integrates 10-100s of cores and has a heterogeneous mix of general-purpose compute engines and highly specialized accelerators. Traditionally, computer architects have relied on tools like architectural simulators (e.g., Accel-Sim, gem5, gem5-SALAM, GPGPU-Sim, MGPUSim, Sniper-Sim, and ZSim) to accurately perform early stage prototyping and optimizations for the proposed research. However, as systems become increasingly complex and heterogeneous, architectural tools are straining to keep up. In particular, publicly available architectural simulators are often not very representative of the industry parts they intend to represent. This leads to a mismatch in expectations; when prototyping new optimizations in gem5 users may draw the wrong conclusions about the efficacy of proposed optimizations if the tool’s models do not provide high fidelity. In this work, we focus on the gem5 simulator, the most popular platform for computer system simulation. In recent years gem5 has been used by ∼20% of simulation-based papers published in top-tier computer architecture conferences per year. Moreover, gem5 can run entire systems, including CPUs, GPUs, and accelerators as well as the operating system, runtime, network and other related components (including multiple ISAs). Thus, gem5 has the potential to allow users to study the behavior of the entire heterogeneous systems. Unfortunately, some of gem5’s models do not always provide high accuracy relative to their ”real” counterparts. In particular, although gem5’s GPU model provides high accuracy internally at AMD [9], the publicly available gem5 GPU model is often inaccurate, especially for the memory subsystem. To understand this, we designed a series of microbenchmarks designed to expose the latencies, bandwidths, and sizes of a variety of GPU components on real AMD GPUs. Our results showed that while gem5’s GPU microarchitecture was relatively accurate (within 5-10% in most cases), gem5’s memory subsytem was off by an average of 272% (645% max) for latency and 70% (693% max) for bandwidth. Accordingly, to help bridge this divide, we propose to design and use a new tool, GPU Accuracy Profiler (GAP), to compare and improve the behavior of gem5’s simulated GPUs relative to real GPUs. By iteratively applying fixes and improvements to gem’s GPU model via GAP, we will significantly improved its fidelity relative to real AMD GPUs. Although this work is still ongoing, our preliminary results show significant promise: on average 25% error for latency and 16% error for bandwidth, respectively. Overall, by completing this work we hope to enable more widespread adoption of gem5 as an accurate platform for heterogeneous architecture research. 

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