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1. GPU Adaptive In-situ Parallel Analytics (GAP)

   Haoyuan Xing, Gagan Agrawal (October 2022, Proceedings of PACT 2022)

   Despite the popularity of in-situ analytics in scientific computing, there is only limited work to date on in-situ analytics for simulations running on GPUs. Notably, two unaddressed challenges are 1) performing memory-efficient in-situ analysis on accelerators and 2)automatically choosing the processing resources and suitable data representation for a given query and platform. This paper addresses both problems. First, GAP makes several new contributions toward making bitmap indices suitable, effective, and efficient as a compressed data summary structure for the GPUs - this includes introducing a layout structure, a method for generating multi-attribute bitmaps, and novel techniques for bitmap-based processing of major operators that comprise complex data analytics. Second, this paper presents a performance modeling methodology, aiming to predict the placement (i.e., CPU or GPU) and the data representation choice (summarization or original) that yield the best performance on a given configuration. Our extensive evaluation of complex in-situ queries and real-world simulations shows that with our methods, analytics on GPU using bitmaps almost always outperforms other options, and the GAP performance model predicts the optimal placement and data representation for most scenarios. 

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2. Accelerating Random Forest Classification on GPU and FPGA

   https://doi.org/10.1145/3545008.3545067  

   Shah, Milan; Neff, Reece; Wu, Hancheng; Minutoli, Marco; Tumeo, Antonino; Becchi, Michela (August 2022, ICPP '22: Proceedings of the 51st International Conference on Parallel Processing)

   Random Forests (RFs) are a commonly used machine learning method for classification and regression tasks spanning a variety of application domains, including bioinformatics, business analytics, and software optimization. While prior work has focused primarily on improving performance of the training of RFs, many applications, such as malware identification, cancer prediction, and banking fraud detection, require fast RF classification. In this work, we accelerate RF classification on GPU and FPGA. In order to provide efficient support for large datasets, we propose a hierarchical memory layout suitable to the GPU/FPGA memory hierarchy. We design three RF classification code variants based on that layout, and we investigate GPU- and FPGA-specific considerations for these kernels. Our experimental evaluation, performed on an Nvidia Xp GPU and on a Xilinx Alveo U250 FPGA accelerator card using publicly available datasets on the scale of millions of samples and tens of features, covers various aspects. First, we evaluate the performance benefits of our hierarchical data structure over the standard compressed sparse row (CSR) format. Second, we compare our GPU implementation with cuML, a machine learning library targeting Nvidia GPUs. Third, we explore the performance/accuracy tradeoff resulting from the use of different tree depths in the RF. Finally, we perform a comparative performance analysis of our GPU and FPGA implementations. Our evaluation shows that, while reporting the best performance on GPU, our code variants outperform the CSR baseline both on GPU and FPGA. For high accuracy targets, our GPU implementation yields a 5-9 × speedup over CSR, and up to a 2 × speedup over Nvidia’s cuML library. 

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3. SEP-Graph: finding shortest execution paths for graph processing under a hybrid framework on GPU

   Hao Wang, Liang Geng (January 2019, Proceedings of 24th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP 2019))

   In general, the performance of parallel graph processing is determined by three pairs of critical parameters, namely synchronous or asynchronous execution mode (Sync or Async), Push or Pull communication mechanism (Push or Pull), and Data-driven or Topology-driven traversing scheme (DD or TD), which increases the complexity and sophistication of programming and system implementation of GPU. Existing graph-processing frameworks mainly use a single combination in the entire execution for a given application, but we have observed their variable and suboptimal performance. In this paper, we present SEP-Graph, a highly efficient software framework for graph-processing on GPU. The hybrid execution mode is automatically switched among three pairs of parameters, with an objective to achieve the shortest execution time in each iteration. We also apply a set of optimizations to SEP-Graph, considering the characteristics of graph algorithms and underlying GPU architectures. We show the effectiveness of SEP-Graph based on our intensive and comparative performance evaluation on NVIDIA 1080, P100, and V100 GPUs. Compared with existing and representative GPU graph-processing framework Groute and Gunrock, SEP-Graph can reduce execution time up to 45.8 times and 39.4 times. 

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4. AGILE: Lightweight and Efficient Asynchronous GPU-SSD Integration

   Yang, Zhuoping; Zhuang, Jinming; Chen, Xingzhen; Jones, Alex; Zhou, Peipei (November 2025, ACM)

   GPUs are critical for compute-intensive applications, yet emerging workloads such as recommender systems, graph analytics, and data analytics often exceed GPU memory capacity. Existing solutions allow GPUs to use CPU DRAM or SSDs as external memory, and the GPU-centric approach enables GPU threads to directly issue NVMe requests, further avoiding CPU intervention. However, current GPU-centric approaches adopt synchronous I/O, forcing threads to stall during long communication delays. We propose AGILE, a lightweight asynchronous GPU-centric I/O library that eliminates deadlock risks and integrates a flexi- ble HBM-based software cache. AGILE overlaps computation and I/O, improving performance by up to 1.88×across workloads with diverse computation-to-communication ratios. Compared to BaM on DLRM, AGILE achieves up to 1.75×speedup through efficient design and overlapping; on graph applications, AGILE reduces soft- ware cache overhead by up to 3.12×and NVMe I/O overhead by up to 2.85×; AGILE also lowers per-thread register usage by up to 1.32×. 

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5. 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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