High-performance computing, scientific data, progressive compression, advanced GPUs
HP-MDR: High-performance and Portable Data Refactoring and Progressive Retrieval with Advanced GPUs
Scientific applications produce vast amounts of data, posing grand challenges in the underlying data management and analytic tasks. Progressive compression is a promising way to address this problem, as it allows for on-demand data retrieval with significantly reduced data movement cost. However, most existing progressive methods are designed for CPUs, leaving a gap for them to unleash the power of today's heterogeneous computing systems with GPUs. In this work, we propose HP-MDR, a high-performance and portable data refactoring and progressive retrieval framework for GPUs.
Executive Impact
HP-MDR delivers a breakthrough in data refactoring and progressive retrieval, achieving up to 13.68x throughput for refactoring and 6.31x for retrieval compared to state-of-the-art CPU-based methods. Its GPU-accelerated pipelines and optimized bitplane encoding ensure scalability and efficiency for exascale systems, offering precise error control and portability across diverse GPU architectures like NVIDIA H100 and AMD MI250X.
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Refactoring Throughput Increase
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Retrieval Throughput Increase
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End-to-End Retrieval Performance
Deep Analysis & Enterprise Applications
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This paper tackles the challenges of data management in high-performance computing (HPC) environments, especially with the advent of exascale systems. The proposed HP-MDR framework leverages the parallel processing capabilities of modern GPUs to overcome limitations of CPU-centric progressive compression methods, significantly enhancing data refactoring and retrieval throughput.
The work addresses the immense volume of scientific data generated by cutting-edge applications, which often overwhelms existing storage and transfer systems. HP-MDR provides a solution for efficient data reduction and on-demand retrieval, crucial for diverse scientific analytics that require varying levels of precision and error control.
Progressive compression is a key technique explored, allowing for incremental data retrieval with user-specified error control. HP-MDR optimizes bitplane encoding and hybrid lossless compression for GPUs, enabling high-performance progressive retrieval, a feature previously limited by CPU-based implementations.
A core focus is on utilizing advanced GPUs (e.g., NVIDIA H100, AMD MI250X) to accelerate the entire data refactoring and retrieval pipeline. HP-MDR includes tailored optimizations for GPU architectures, ensuring portability and maximizing throughput by carefully managing memory access patterns and inter-thread communication.
13.68x
Average Throughput Increase in Data Refactoring
HP-MDR significantly boosts data refactoring throughput, outperforming state-of-the-art CPU-based solutions by an average of 13.68 times. This indicates superior efficiency in preparing scientific data for storage and analysis on GPU-accelerated systems.
Enterprise Process Flow
Multi-level Decomposer
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Optimized Bitplane Encoder
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Hybrid Lossless Encoder
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Compressed Bitplanes (Storage)
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Case Study: Scalability on Exascale Systems (JHTDB Dataset)
Evaluations on the Frontier supercomputer (AMD MI250X GPUs) demonstrated HP-MDR's near-linear scalability for end-to-end refactoring and reconstruction. Achieving 90% and 89% of ideal speed-up on H100 and MI250X respectively, the framework proves its capability to handle massive scientific datasets efficiently across multiple nodes and GPUs.
Advanced ROI Calculator
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Estimated Annual Savings
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Implementation Roadmap
Our phased approach ensures a smooth, efficient, and impactful AI integration within your enterprise.
Phase 1: GPU Kernel Optimization (4-6 Weeks)
Refactoring and optimizing bitplane encoding and hybrid lossless compression kernels for maximum GPU utilization and portability across NVIDIA and AMD architectures.
Phase 2: Pipeline Integration & Refinement (6-8 Weeks)
Integrating optimized kernels into an end-to-end refactoring and reconstruction pipeline, with advanced CPU-GPU memory overlap techniques to hide latencies.
Phase 3: QoI Error Control & Validation (3-5 Weeks)
Implementing and validating progressive retrieval with guaranteed error control for Quantities of Interest (QoIs), ensuring scientific fidelity.
Phase 4: Scalability & Benchmarking (4-7 Weeks)
Conducting extensive scalability tests on exascale systems using real-world datasets to confirm high performance across diverse scales.
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