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1. 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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2. 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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3. gem5 GPU Accuracy Profiler (GAP)

   Charles Jamieson, Anushka Chandrashekar (June 2022, 4th 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, we currently do not have a way to model validated gem5 configurations for the most recent AMD GPUs. Current support focuses on Carrizo- and Vega-class GPUs. Unfortunately, these models do not always provide high accuracy relative to real GPU runs. This leads to a mismatch between how each instruction is supposedly being executed according to the ISA and how a given GPU model executes a given instruction. These discrepancies are of interest to those developing the gem5 GPU models as they can lead to less accurate simulations. Accordingly, to help bridge this divide, we have created a new tool, GAP (gem5 GPU Accuracy Profiler), to identify discrepancies between real GPU and simulated gem5 GPU behavior. GAP identifies and verifies how accurate these configurations relative to real GPUs by comparing the simulator’s performance counters to those from real GPUs. 

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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. Analyzing the Benefits of More Complex Cache Replacement Policies in Moderns GPU LLCs

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

   The gem5 simulator offers Classic and Ruby as two separate memory models for simulating on-chip caches. The Classic model, which originated from M5, is a quick and simple option that allows for easy configuration, but only supports a basic MOESI coherence protocol. On the other hand, the Ruby model, which was developed by GEMS [2], is a more advanced and flexible option that can accurately simulate a wider range of cache coherence protocols and features. However, choosing between the two memory system models in gem5 is challenging for researchers as each has advantages and limitations which can be inconvenient. In particular, this has led to a bifurcation of effort where prior work has added replacement policies to Classic and Ruby in parallel – duplicating effort unnecessarily and preventing users from using a desired replacement policy if it is not implemented in the desired memory model (e.g., users could only use RRIP in Classic). Accordingly, we merged the cache replacement policies from Classic to Ruby, enabling users to use any of the replacement policies in either memory model. Gem5 currently has the capability to support 13 replacement policies, which can be used exchangeable within the Classic and Ruby cache models, including commonly used options like LRU, FIFO, PseudoLRU, and different types of RRIPs. After combining the replacement policies for the Classic and Ruby cache models, we designed and integrated (into gem5’s nightly regressions) multiple corner case tests to verify and ensure the continued correct functionality of these policies. Through these tests, we identified and fixed several bugs to ensure that the replacement policies operate correctly. Finally, with the newly enabled and verified functionality, since there is limited information about how different replacement policies affects GPU performance, we decided to use gem5 to study these policies in a GPU context. Specifically, we study GPU L2 caches, since GPU L1 caches are often used to stream data through and thus are unlikely to be significantly impacted by replacement policy. 

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