Deploying resource-intensive Video LLMs often relies on Split DNNs, yet this introduces a severe transmission bottleneck-intermediate embeddings 1 can be$26 \times$larger than original videos. Lossy compression is an effective solution to the I/O bottleneck in machine learning [1]. However, no compression solution exists to address the high dimensionality and unique layout of these embeddings.

Next-generation sequencing (NGS) platforms generate multi-terabyte data, making compression a major bottleneck for storage, transfer, and downstream analysis.

Large-scale scientific applications generate massive floating-point data, making lossy compression an essential method to reduce storage needs and improve performance. However, variations in compression time for various reasons can negatively impact scheduling and workload balance. Existing empirical approaches lack accuracy and generalizability [1]. This work proposes a novel analytical method for accurately predicting the compression time of prediction-based lossy compressors. The method decomposes the compression process into four stages: prediction and quantization, frequency counting and codebook construction, Huffman encoding, and additional lossless encoding. By using the statistics method, we capture the causes of time variation and eliminates application-specific dependencies. We design a surrogate model to predict the time cost: using sampling and offline testing to get the key parameters, then use regression and some probability models to make the prediction.

Scientific applications generate massive amounts of data, leading to significant storage and I/O bottlenecks. Lossy compression is a crucial technique for reducing data volume in scientific computing, balancing storage efficiency and data fidelity. Such scientific data often consists of multiple fields representing various physical metrics, which exhibit inherent correlations. However, existing compression solutions predominantly rely on local information within a single field, overlooking cross-field correlations that could enhance compression efficiency. In this paper, we introduce a novel compression framework that integrates both local and cross-field information to enhance predictive accuracy. With the help of a carefully designed neural network, our approach effectively captures complex correlations across fields, significantly improving compression ratios. Additionally, our method can rely solely on cross-field prediction to reconstruct fields, achieving extremely high compression ratios under relaxed accuracy constraints. We evaluate our approach on two real-world scientific applications across 85 data fields, demonstrating compression ratio improvements of up to 103.4% for a single field and up to 19.3% overall.

SZ3 (0.07MB, PSNR = 29.2) (c)NeurLZ (0.07MB, PSNR = 39.1)

Second-order optimization methods have been developed to enhance convergence and generalization in deep neural network (DNN) training compared to first-order methods like Stochastic Gradient Descent (SGD). However, these methods face challenges in distributed settings due to high communication overhead. Gradient compression, a technique commonly used to accelerate communication for first-order approaches, often results in low communication reduction ratios, decreased model accuracy, and/or high compression overhead when applied to second-order methods. To address these limitations, we introduce a novel gradient compression method for second-order optimizers called COMPSO. This method effectively reduces communication costs while preserving the advantages of second-order optimization. COMPSO employs stochastic rounding to maintain accuracy and filters out minor gradients to improve compression ratios. Additionally, we develop GPU optimizations to minimize compression overhead and performance modeling to ensure end-to-end performance gains across various systems. Evaluation of COMPSO on different DNN models shows that it achieves a compression ratio of 22.1×, reduces communication time by 14.2×, and improves overall performance by 1.9×, all without any drop in model accuracy.

Over the last nearly 20 years, lossy compression has become an essential aspect of HPC applications’ data pipelines, allowing them to overcome limitations in storage capacity and bandwidth and, in some cases, increase computational throughput and capacity. However, with the adoption of lossy compression comes the requirement to assess and control the impact lossy compression has on scientific outcomes. In this work, we take a major step forward in describing the state of practice and by characterizing workloads. We examine applications’ needs and compressors’ capabilities across 9 different supercomputing application domains. We present 24 takeaways that provide best practices for applications, operational impacts for facilities achieving compressed data, and gaps in application needs not addressed by production compressors that point towards opportunities for future compression research.

Error-bounded lossy compression is one of the most efficient solutions to reduce the volume of scientific data. For lossy compression, progressive decompression and random-access decompression are critical features that enable on-demand data access and flexible analysis workflows. However, these features can severely degrade compression quality and speed. To address these limitations, we propose a novel streaming compression framework that supports both progressive decompression and random-access decompression while maintaining high compression quality and speed. Our contributions are three-fold: (1) we design the first compression framework that simultaneously enables both progressive decompression and random-access decompression; (2) we introduce a hierarchical partitioning strategy to enable both streaming features, along with a hierarchical prediction mechanism that mitigates the impact of partitioning and achieves high compression quality—even comparable to state-of-the-art (SOTA) non-streaming compressor SZ3; and (3) our framework delivers high compression and decompression speed, up to 6.7 × faster than SZ3.

Cross-Silo federated learning is widely used for scaling deep neural network (DNN) training over data silos from different locations worldwide while guaranteeing data privacy. Communication has been identified as the main bottleneck when training large-scale models due to large-volume model parameters and gradient transmission across public networks with limited bandwidth. Most previous works focus on gradient compression, while limited work tries to compress parameters that can not be ignored and extremely affect communication performance during the training. To bridge this gap, we proposeFedCSpc: an efficient cross-silo federated learning system with an XAI-driven adaptive parameter compression strategy for large-scale model training. Our work substantially differs from existing gradient compression techniques due to the distinct data features of gradient and parameter. The key contributions of this paper are fourfold. (1) Our designedFedCSpcproposes to compress the parameter during the training using the state-of-the-art error-bounded lossy compressor – SZ3. (2) We develop an adaptive compression error bound adjustment algorithm to guarantee the model accuracy effectively. (3) We exploit an efficient approach to utilize the idle CPU resources of clients to compress the parameters. (4) We perform a comprehensive evaluation with a wide range of models and benchmarks on a GPU cluster with 65 GPUs. Results show thatFedCSpccan achieve the same model accuracy as FedAvg while reducing the data volume of parameters and gradients in communication by up to 7.39× and 288×, respectively. With 32 clients on a 4Gb size model,FedCSpcsignificantly outperforms FedAvg in wall-clock time in the emulated WAN environment (at the bandwidth of 1 Gbps or lower without loss of generality).

As time series data become popular, their volume increases rapidly. Time series databases are designed for such data, and they process data in short slices, meaning that the compression units for compressors are small. How to compress the short slices of floating-points while reserving a high compression ratio and a high decompression speed remains a problem.To solve the problem, we propose a lossy compressor Machete. It uses an efficient hybrid encoder of Huffman encoding and variable length quantity (VLQ). Adaptive encoding selection makes it excel on short-slice data compression ratio, while the simple framework ensures fast decompression. We also find a limitation in VLQ and propose the optimal VLQ to further improve the compression ratio.Our evaluation on four real-world datasets shows that Machete outperforms state-of-the-art compressors by 32%−80% on compression ratio and achieves the fastest decompression speed on two datasets. When applied to a well-known time series database InfluxDB, Machete saves disk usage up to 79% and improves the query performance of the InfluxDB database by saving I/O.

Lossy compression and asynchronous I/O are two of the most effective solutions for reducing storage overhead and enhancing I/O performance in large-scale high-performance computing (HPC) applications. However, current approaches have limitations that prevent them from fully leveraging lossy compression, and they may also result in task collisions, which restrict the overall performance of HPC applications. To address these issues, we propose an optimization approach for the task scheduling problem that encompasses computation, compression, and I/O. Our algorithm adaptively selects the optimal compression and I/O queue to minimize the performance degradation of the computation. We also introduce an intra-node I/O workload balancing mechanism that evenly distributes the workload across different processes. Additionally, we design a framework that incorporates fine-grained compression, a compressed data buffer, and a shared Huffman tree to fully benefit from our proposed task scheduling. Experimental results with up to 16 nodes and 64 GPUs from ORNL Summit, as well as real-world HPC applications, demonstrate that our solution reduces I/O overhead by up to 3.8× and 2.6× compared to non-compression and asynchronous I/O solutions, respectively.

Error-bounded lossy compression has been identified as a promising solution for significantly reducing scientific data volumes upon users' requirements on data distortion. For the existing scientific error-bounded lossy compressors, some of them (such as SPERR and FAZ) can reach fairly high compression ratios and some others (such as SZx, SZ, and ZFP) feature high compression speeds, but they rarely exhibit both high ratio and high speed meanwhile. In this paper, we propose HPEZ with newly-designed interpolations and quality-metric-driven auto-tuning, which features significantly improved compression quality upon the existing high-performance compressors, meanwhile being exceedingly faster than high-ratio compressors. The key contributions lie as follows: (1) We develop a series of advanced techniques such as interpolation re-ordering, multi-dimensional interpolation, and natural cubic splines to significantly improve compression qualities with interpolation-based data prediction. (2) The auto-tuning module in HPEZ has been carefully designed with novel strategies, including but not limited to block-wise interpolation tuning, dynamic dimension freezing, and Lorenzo tuning. (3) We thoroughly evaluate HPEZ compared with many other compressors on six real-world scientific datasets. Experiments show that HPEZ outperforms other high-performance error-bounded lossy compressors in compression ratio by up to 140% under the same error bound, and by up to 360% under the same PSNR. In parallel data transfer experiments on the distributed database, HPEZ achieves a significant performance gain with up to 40% time cost reduction over the second-best compressor.

Today's scientific simulations require significant data volume reduction because of the enormous amounts of data produced and the limited I/O bandwidth and storage space. Error-bounded lossy compression has been considered one of the most effective solutions to the above problem. However, little work has been done to improve error-bounded lossy compression for Adaptive Mesh Refinement (AMR) simulation data. Unlike the previous work that only leverages 1D compression, in this work, we propose an approach (TAC) to leverage high-dimensional SZ compression for each refinement level of AMR data. To remove the data redundancy across different levels, we propose several pre-process strategies and adaptively use them based on the data features. We further optimizeTACtoTAC+by improving the lossless encoding stage of SZ compression to handle many small AMR data blocks after the pre-processing efficiently. Experiments on 10 AMR datasets from three real-world large-scale AMR simulations demonstrate thatTAC+can improve the compression ratio by up to 4.9× under the same data distortion, compared to the state-of-the-art method. In addition, we leverage the flexibility of our approach to tune the error bound for each level, which achieves much lower data distortion on two application-specific metrics.

The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data. To maintain the usability of scientific data, error-bound lossy compression is proposed and developed as an essential technique for the size reduction of scientific data with constrained data distortion. Among the diverse datasets generated by various scientific simulations, certain datasets cannot be effectively compressed by existing error-bounded lossy compressors with traditional techniques. The recent success of Artificial Intelligence has inspired several researchers to integrate neural networks into error-bounded lossy compressors. However, those works still suffer from limited compression ratios and/or extremely low efficiencies. To address those issues and improve the compression on the hard-to-compress datasets, in this paper, we propose SRNNSZ, which is a deep learning-based scientific error-bounded lossy compressor leveraging the hierarchical data grid expansion paradigm implemented by super-resolution neural networks. SRNN-SZ applies the most advanced super-resolution network HAT for its compression, which is free of time-costing per-data training. In experiments compared with various state-of-the-art compressors, SRNN-SZ achieves up to 75% compression ratio improvements under the same error bound and up to 80% compression ratio improvements under the same PSNR than the second-best compressor.

This paper considers spatial-domain detector design for mutual interference mitigation among automotive MIMO-FMCW radars. This detector design is based on our previously derived interference signal model that fully accounts for the time-frequency incoherence and the slow-time code incoherence between the victim and interfering radars. Compared with our previous spatial-domain detector in [1], the proposed detector further exploits the structural property of both transmit and receive steering vectors of the interference for stronger interference mitigation. Preliminary numerical results confirm the performance of our proposed detector and show advantages over baseline detectors.

As supercomputers advance towards exascale capabilities, computational intensity increases significantly, and the volume of data requiring storage and transmission experiences exponential growth. Adaptive Mesh Refinement (AMR) has emerged as an effective solution to address these two challenges. Concurrently, error-bounded lossy compression is recognized as one of the most efficient approaches to tackle the latter issue. Despite their respective advantages, few attempts have been made to investigate how AMR and error-bounded lossy compression can function together. To this end, this study presents a novel in-situ lossy compression framework that employs the HDF5 filter to improve both I/O costs and boost compression quality for AMR applications. We implement our solution into the AMReX framework and evaluate on two real-world AMR applications, Nyx and WarpX, on the Summit supercomputer. Experiments with 4096 CPU cores demonstrate that AMRIC improves the compression ratio by up to 81× and the I/O performance by up to 39× over AMReX's original compression solution.

Deep neural networks (DNNs) have achieved remarkable success in many fields. However, large-scale DNNs also bring storage costs when storing snapshots for preventing clusters’ frequent failures or incur significant communication overheads when transmitting DNNs in the Federated Learning (FL). Recently, several approaches, such as Delta-DNN and LC-Checkpoint, aim to reduce the size of DNNs’ snapshot storage by compressing the difference between two neighboring versions of the DNNs (a.k.a., delta). However, we observe that existing approaches, applying traditional global lossy quantization techniques in DNN's delta compression, can not fully exploit the data similarity since the parameters’ value ranges vary among layers. To fully explore the similarity of the delta model and improve the compression ratio, we propose a quantization-based local-sensitive delta compression approach, named QD-Compressor, by developing a layer-based local-sensitive quantization scheme and error feedback mechanism. Specifically, the quantizers and number of quantization bits are adaptive among layers based on the value distribution and weighted entropy of the delta's parameters. To avoid quantization error degrading the performance of the restored model, an alternative error feedback mechanism is designed to dynamically correct the quantization error during the training process. Experiments on multiple popular DNNs and datasets show that QD-Compressor obtains a higher 7×-40× compression ratio in the model snapshot compression scenario than the state-of-the-art approaches. Additionally, QD-Compressor achieves an 11×-15× compression ratio to the residual model of the Federated Learning compression scenario.

Today's scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage space. Error-bounded lossy compression has been considered one of the most effective solutions to the above problem. However, little work has been done to improve error-bounded lossy compression for Adaptive Mesh Refinement (AMR) simulation data. Unlike the previous work that only leverages 1D compression, in this work, we propose to leverage high-dimensional (e.g., 3D) compression for each refinement level of AMR data. To remove the data redundancy across different levels, we propose three pre-process strategies and adaptively use them based on the data characteristics. Experiments on seven AMR datasets from a real-world large-scale AMR simulation demonstrate that our proposed approach can improve the compression ratio by up to 3.3X under the same data distortion, compared to the state-of-the-art method. In addition, we leverage the flexibility of our approach to tune the error bound for each level, which achieves much lower data distortion on two application-specific metrics.

Error-bounded lossy compression is one of the most effective techniques for reducing scientific data sizes. However, the traditional trial-and-error approach used to configure lossy compressors for finding the optimal trade-off between reconstructed data quality and compression ratio is prohibitively expensive. To resolve this issue, we develop a general-purpose analytical ratio-quality model based on the prediction-based lossy compression framework, which can effectively foresee the reduced data quality and compression ratio, as well as the impact of lossy compressed data on post-hoc analysis quality. Our analytical model significantly improves the prediction-based lossy compression in three use-cases: (1) optimization of predictor by selecting the best-fit predictor; (2) memory compression with a target ratio; and (3) in-situ compression optimization by fine-grained tuning error-bounds for various data partitions. We evaluate our analytical model on 10 scientific datasets, demonstrating its high accuracy (93.47% accuracy on average) and low computational cost (up to 18.7x lower than the trial-and-error approach) for estimating the compression ratio and the impact of lossy compression on post-hoc analysis quality. We also verify the high efficiency of our ratio-quality model using different applications across the three use-cases. In addition, our experiment demonstrates that our modeling-based approach reduces the time to store the 3D RTM data with HDF5 by up to 3.4 x with 128 CPU cores over the traditional solution.

As HPC systems continue to grow to exascale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors to reduce the data size and improve the I/O performance. However, little work has been done for effectively offloading lossy compression onto FPGA-based SmartNICs to reduce the compression overhead. In this paper, we propose a hardware-algorithm co-design for an efficient and adaptive lossy compressor for scientific data on FPGAs (called CEAZ), which is the first lossy compressor that can achieve high compression ratios and throughputs simultaneously. Specifically, we propose an efficient Huffman coding approach that can adaptively update Huffman codewords online based on codewords generated offline, from a variety of representative scientific datasets. Moreover, we derive a theoretical analysis to support a precise control of compression ratio under an error-bounded compression mode, enabling accurate offline Huffman codewords generation. This also helps us create a fixed-ratio compression mode for consistent throughput. In addition, we develop an efficient compression pipeline by adopting cuSZ's dual-quantization algorithm to our hardware use cases. Finally, we evaluate CEAZ on five real-world datasets with both a single FPGA board and 128 nodes (to accelerate parallel I/O). Experiments show that CEAZ outperforms the second-best FPGA-based lossy compressor by 2.3X of throughput and 3.0X of ratio. It also improves MPI_File_write and MPI_Gather throughputs by up to 28.9X and 37.8X, respectively.

Lossy compression is one of the most efficient solutions to reduce storage overhead and improve I/O performance for HPC applications. However, existing parallel I/O libraries cannot fully utilize lossy compression to accelerate parallel write due to the lack of deep understanding on compression-write performance. To this end, we propose to deeply integrate predictive lossy compression with HDF5 to significantly improve the parallel-write performance. Specifically, we propose analytical models to predict the time of compression and parallel write before the actual compression to enable compression-write overlapping. We also introduce an extra space in the process to handle possible data overflows resulting from prediction uncertainty in compression ratios. Moreover, we propose an optimization to reorder the compression tasks to increase the overlapping efficiency. Experiments with up to 4,096 cores from Summit show that our solution improves the write performance by up to$4.5\times$and$2.9\times$over the non-compression and lossy compression solutions, respectively, with only 1.5% storage overhead (compared to original data) on two real-world HPC applications.

Vast volumes of data are produced by today's scientific simulations and advanced instruments. These data cannot be stored and transferred efficiently because of limited I/O bandwidth, network speed, and storage capacity. Error-bounded lossy compression can be an effective method for addressing these issues: not only can it significantly reduce data size, but it can also control the data distortion based on user-defined error bounds. In practice, many scientific applications have specific requirements or constraints for lossy compression, in order to guarantee that the reconstructed data are valid for post hoc analysis. For example, some datasets contain irrelevant data that should be isolated in particular and users often have intuition regarding value ranges, geospatial regions, and other data subsets that are crucial for subsequent analysis. Existing state-of-the-art error-bounded lossy compressors, however, do not consider these constraints during compression, resulting in inferior compression ratios with respect to user's post hoc analysis, due to the fact that the data itself provides little or no value for post hoc analysis. In this work we address this issue by proposing an optimized framework that can preserve diverse constraints during the error-bounded lossy compression, e.g., cleaning the irrelevant data, efficiently preserving different precision for multiple value intervals, and allowing users to set diverse precision over both regular and irregular regions. We perform our evaluation on a supercomputer with up to 2,100 cores. Experiments with six real-world applications show that our proposed diverse constraints based error-bounded lossy compressor can obtain a higher visual quality or data fidelity on reconstructed data with the same or even higher compression ratios compared with the traditional state-of-the-art compressor SZ. Our experiments also demonstrate very good scalability in compression performance compared with the I/O throughput of the parallel file system.

In recent years, the increasing complexity in scientific simulations and emerging demands for training heavy artificial intelligence models require massive and fast data accesses, which urges high-performance computing (HPC) platforms to equip with more advanced storage infrastructures such as solid-state disks (SSDs). While SSDs offer high-performance I/O, the reliability challenges faced by the HPC applications under the SSD-related failures remains unclear, in particular for failures resulting in data corruptions. The goal of this paper is to understand the impact of SSD-related faults on the behaviors of complex HPC applications. To this end, we propose FFIS, a FUSE-based fault injection framework that systematically introduces storage faults into the application layer to model the errors originated from SSDs. FFIS is able to plant different I/O related faults into the data returned from underlying file systems, which enables the investigation on the error resilience characteristics of the scientific file format. We demonstrate the use of FFIS with three representative real HPC applications, showing how each application reacts to the data corruptions, and provide insights on the error resilience of the widely adopted HDF5 file format for the HPC applications.

Error-bounded lossy compression is becoming an indispensable technique for the success of today’s scientific projects with vast volumes of data produced during the simulations or instrument data acquisitions. Not only can it significantly reduce data size, but it also can control the compression errors based on user-specified error bounds. Autoencoder (AE) models have been widely used in image compression, but few AE-based compression approaches support error-bounding features, which are highly required by scientific applications. To address this issue, we explore using convolutional autoencoders to improve error-bounded lossy compression for scientific data, with the following three key contributions. (1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model. (2) We optimize the compression quality for main stages in our designed AE-based error-bounded compression framework, fine-tuning the block sizes and latent sizes and also optimizing the compression efficiency of latent vectors. (3) We evaluate our proposed solution using five real-world scientific datasets and comparing them with six other related works. Experiments show that our solution exhibits a very competitive compression quality from among all the compressors in our tests. In absolute terms, it can obtain a much better compression quality (100% $\sim$ 800% improvement in compression ratio with the same data distortion) compared with SZ2.1 and ZFP in cases with a high compression ratio.

Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous high-performance computing (HPC) architecture, GPU-accelerated error-bounded compressors (such as CUSZ and cuZFP) have been developed. However, they suffer from either low performance or low compression ratios. To this end, we propose CUSZ+ to target both high compression ratios and throughputs. We identify that data sparsity and data smoothness are key factors for high compression throughputs. Our key contributions in this work are fourfold: (1) We propose an efficient compression workflow to adaptively perform run-length encoding and/or variable-length encoding. (2) We derive Lorenzo reconstruction in decompression as multidimensional partial-sum computation and propose a fine-grained Lorenzo reconstruction algorithm for GPU architectures. (3) We carefully optimize each of CUSZ kernels by leveraging state-of-the-art CUDA parallel primitives. (4) We evaluate CU SZ+ using seven real-world HPC application datasets on V100 and A100 GPUs. Experiments show CUSZ+ improves the compression throughputs and ratios by up to 18.4× and 5.3×, respectively, over CUSZ on the tested datasets.

Vast volumes of scientific data cannot be stored and transferred efficiently because of limited I/O bandwidth, network bandwidth, and storage capacity. Error-bounded lossy compression can be an effective method for resolving these big data issues, since not only can it significantly reduce the data size but it can also control the data distortion based on user-defined error bounds. In practice, many scientific applications have specific data fidelity requirements across different value ranges/intervals of the dataset for the lossy compression, in order to guarantee that the reconstructed data are valid for post hoc analysis. Existing state-of-the-art error-bounded lossy compressors, however, do not support multi-range based error-bounds in the lossy compression, leaving a critical gap that hampers their effective use in practice. In this work, we address this issue by proposing a multi-range based error-bounded lossy compressor based on the state-of-the-art SZ lossy compressor. Our approach allows users to set different error bounds in different value ranges for a compressoin task. We evaluate our approach on several real-world datasets and show that it can obtain a higher visual quality or data fidelity on reconstructed data with the same or even higher compression ratios achieved by SZ.

Extreme-scale cosmological simulations have been widely used by today's researchers and scientists on leadership supercomputers. A new generation of error-bounded lossy compressors has been used in workflows to reduce storage requirements and minimize the impact of throughput limitations while saving large snapshots of high-fidelity data for post-hoc analysis. In this paper, we propose to adaptively provide compression configurations to compute partitions of cosmological simulations with newly designed post-analysis aware rate-quality modeling. The contribution is fourfold: (1) We propose a novel adaptive approach to select feasible error bounds for different partitions, showing the possibility and efficiency of adaptively configuring lossy compression for each partition individually. (2) We build models to estimate the overall loss of post-analysis result due to lossy compression and to estimate compression ratio, based on the property of each partition. (3) We develop an efficient optimization guideline to determine the best-fit configuration of error bounds combination in order to maximize the compression ratio under acceptable post-analysis quality loss. (4) Our approach introduces negligible overheads for feature extraction and error-bound optimization for each partition, enabling post-analysis-aware in situ lossy compression for cosmological simulations. Experiments show that our proposed models are highly accurate and reliable. Our fine-grained adaptive configuration approach improves the compression ratio of up to 73% on the tested datasets with the same post-analysis distortion with only 1% performance overhead.

Convolutional neural networks (CNNs) are becoming increasingly deeper, wider, and non-linear because of the growing demand on prediction accuracy and analysis quality. The wide and deep CNNs, however, require a large amount of computing resources and processing time. Many previous works have studied model pruning to improve inference performance, but little work has been done for effectively reducing training cost. In this paper, we propose ClickTrain: an efficient and accurate end-to-end training and pruning framework for CNNs. Different from the existing pruning-during-training work, ClickTrain provides higher model accuracy and compression ratio via fine-grained architecture-preserving pruning. By leveraging pattern-based pruning with our proposed novel accurate weight importance estimation, dynamic pattern generation and selection, and compiler-assisted computation optimizations, ClickTrain generates highly accurate and fast pruned CNN models for direct deployment without any time overhead, compared with the baseline training. ClickTrain also reduces the end-to-end time cost of the state-of-the-art pruning-after-training method by up to 2.3x with comparable accuracy and compression ratio. Moreover, compared with the state-of-the-art pruning-during-training approach, ClickTrain provides significant improvements both accuracy and compression ratio on the tested CNN models and datasets, under similar limited training time.

DNNs are becoming increasingly deeper, wider, and nonlinear due to the growing demands on prediction accuracy and analysis quality. When training a DNN model, the intermediate activation data must be saved in the memory during forward propagation and then restored for backward propagation. Traditional memory saving techniques such as data recomputation and migration either suffers from a high performance overhead or is constrained by specific interconnect technology and limited bandwidth. In this paper, we propose a novel memory-driven high performance CNN training framework that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger neural networks. Specifically, we provide theoretical analysis and then propose an improved lossy compressor and an adaptive scheme to dynamically configure the lossy compression error-bound and adjust the training batch size to further utilize the saved memory space for additional speedup. We evaluate our design against state-of-the-art solutions with four widely-adopted CNNs and the ImangeNet dataset. Results demonstrate that our proposed framework can significantly reduce the training memory consumption by up to 13.5× and 1.8× over the baseline training and state-of-the-art framework with compression, respectively, with little or no accuracy loss. The full paper can be referred to at https://arxiv.org/abs/2011.09017.

With the rising utilization of radars on vehicles, interference among vehicular radars in dense traffic scenarios is an emerging problem awaiting innovative solutions. In this work, we consider incoherent FMCW radar interference cases where different FMCW radars use different chirp slopes. Our simulation shows that under the simplest spectrum access scheme - random frequency division multiplexing (RFDM), the false alarm caused by incoherent interference is a serious issue. We study the interference mitigation performance of three slow-time waveform randomization schemes: frequency hopping (FH), joint RFDM and phase coding (PC), and joint FH and PC. Our comparative study shows that not all slow-time waveform randomization schemes perform well under incoherent interference. FH even achieves worse false alarm performance than RFDM. Joint RFDM and PC achieve the best trade-off between the probability of false alarm and the probability of target detection.

Deep neural networks (DNNs) are becoming increasingly deeper, wider, and non-linear due to the growing demands on prediction accuracy and analysis quality. Training wide and deep neural networks require large amounts of storage resources such as memory because the intermediate activation data must be saved in the memory during forward propagation and then restored for backward propagation. However, state-of-the-art accelerators such as GPUs are only equipped with very limited memory capacities due to hardware design constraints, which significantly limits the maximum batch size and hence performance speedup when training large-scale DNNs. Traditional memory saving techniques either suffer from performance overhead or are constrained by limited interconnect bandwidth or specific interconnect technology. In this paper, we propose a novel memory-efficient CNN training framework (called COMET) that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger models or to accelerate training. Our framework purposely adopts error-bounded lossy compression with a strict error-controlling mechanism. Specifically, we perform a theoretical analysis on the compression error propagation from the altered activation data to the gradients, and empirically investigate the impact of altered gradients over the training process. Based on these analyses, we optimize the error-bounded lossy compression and propose an adaptive error-bound control scheme for activation data compression. Experiments demonstrate that our proposed framework can significantly reduce the training memory consumption by up to 13.5X over the baseline training and 1.8X over another state-of-the-art compression-based framework, respectively, with little or no accuracy loss.

Packet-level wireless (802.11ax or 5G) network simulators face escalating system dimensionality resulting from dense deployment scenarios supporting wideband, Multi-Input Multi-Output (MIMO), Multi-User (MU) transmission. Managing the resulting network simulation complexity and achieving practical runtimes require continuing enhancements to physical (PHY) layer abstractions. This work improves the state-of-the-art PHY layer abstractions via a new computational workflow that maps extensive offline link simulation results for OFDM/OFDMA MIMO/MU-MIMO system performance over frequency-selective fading channels into Packet Error Ratio (PER) for network simulations. The proposed method is shown to require modest additional storage and the runtime is insensitive to the increase in PHY layer dimensionality. We describe the principles of this new method and provide details about its implementation, performance, and validation.

Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain high fidelity for postanalysis. Because supercomputers and HPC applications are becoming heterogeneous using accelerator-based architectures, in particular GPUs, several development teams have recently released GPU versions of their lossy compressors. However, existing state-of-the-art GPU-based lossy compressors suffer from either low compression and decompression throughput or low compression quality. In this paper, we present an optimized GPU version, cuSZ, for one of the best error-bounded lossy compressors-SZ. To the best of our knowledge, cuSZ is the first error-bounded lossy compressor on GPUs for scientific data. Our contributions are fourfold. (1) We propose a dual-quantization scheme to entirely remove the data dependency in the prediction step of SZ such that this step can be performed very efficiently on GPUs. (2) We develop an efficient customized Huffman coding for the SZ compressor on GPUs. (3) We implement cuSZ using CUDA and optimize its performance by improving the utilization of GPU memory bandwidth. (4) We evaluate our cuSZ on five real-world HPC application datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and GPUs. Experiments show that our cuSZ improves SZ's compression throughput by up to 370.1x and 13.1x, respectively, over the production version running on single and multiple CPU cores, respectively, while getting the same quality of reconstructed data. It also improves the compression ratio by up to 3.48x on the tested data compared with another state-of-the-art GPU supported lossy compressor.

Deep neural networks (DNNs) have gained considerable attention in various real-world applications due to the strong performance on representation learning. However, a DNN needs to be trained many epochs for pursuing a higher inference accuracy, which requires storing sequential versions of DNNs and releasing the updated versions to users. As a result, large amounts of storage and network resources are required, significantly hampering DNN utilization on resource-constrained platforms (e.g., IoT, mobile phone).

To help understand our universe better, researchers and scientists currently run extreme-scale cosmology simulations on leadership supercomputers. However, such simulations can generate large amounts of scientific data, which often result in expensive costs in data associated with data movement and storage. Lossy compression techniques have become attractive because they significantly reduce data size and can maintain high data fidelity for post-analysis. In this paper, we propose to use GPU-based lossy compression for extreme-scale cosmological simulations. Our contributions are threefold: (1) we implement multiple GPU-based lossy compressors to our open-source compression benchmark and analysis framework named Foresight; (2) we use Foresight to comprehensively evaluate the practicality of using GPU-based lossy compression on two real-world extreme-scale cosmology simulations, namely HACC and Nyx, based on a series of assessment metrics; and (3) we develop a general optimization guideline on how to determine the best-fit configurations for different lossy compressors and cosmological simulations. Experiments show that GPU-based lossy compression can provide necessary accuracy on post-analysis for cosmological simulations and high compression ratio of 5 ~ 15× on the tested datasets, as well as much higher compression and decompression throughput than CPU-based compressors.

Error-bounded lossy compression is critical to the success of extreme-scale scientific research because of ever-increasing volumes of data produced by today's high-performance computing (HPC) applications. Not only can error-controlled lossy compressors significantly reduce the I/O and storage burden but they can retain high data fidelity for post analysis. Existing state-of-the-art lossy compressors, however, generally suffer from relatively low compression and decompression throughput (up to hundreds of megabytes per second on a single CPU core), which considerably restrict the adoption of lossy compression by many HPC applications especially those with a fairly high data production rate. In this paper, we propose a highly efficient lossy compression approach based on field programmable gate arrays (FPGAs) under the state-of-the-art lossy compression model SZ. Our contributions are fourfold. (1) We adopt a wavefront memory layout to alleviate the data dependency during the prediction for higher-dimensional predictors, such as the Lorenzo predictor. (2) We propose a co-design framework named waveSZ based on the wavefront memory layout and the characteristics of SZ algorithm and carefully implement it by using high-level synthesis. (3) We propose a hardware-algorithm co-optimization method to improve the performance. (4) We evaluate our proposed waveSZ on three real-world HPC simulation datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and FPGAs. Experiments show that our waveSZ can improve SZ's compression throughput by 6.9X ~ 8.7X over the production version running on a state-of-the-art CPU and improve the compression ratio and throughput by 2.1X and 5.8X on average, respectively, compared with the state-of-the-art FPGA design.

With the increasing density of vehicular radars on the road, interference among vehicular radars is becoming an important problem. In this work, we consider a frequency modulated continuous wave (FMCW) radar multiple access network and develop mutual interference models for analyzing target-ghost classification performance. We next define a cross-layer performance metric called multiple access capacity that is analyzed under a simple random access scheme (un-slotted ALOHA) to obtain system design insights.

Existing decentralized coded caching solutions cannot guarantee small loads in the general scenario with arbitrary file sizes and cache sizes. In this paper, we propose an optimization framework for decentralized coded caching in the general scenario to minimize the worst-case load and average load (under an arbitrary file popularity), respectively. Specifically, we first propose a class of decentralized coded caching schemes for the general scenario, which are specified by a general caching parameter and include several known schemes as special cases. Then, we optimize the caching parameter to minimize the worst-case load and average load, respectively. Each of the two optimization problems is a challenging nonconvex problem with a nondifferentiable objective function. For each optimization problem, we develop an iterative algorithm to obtain a stationary point using techniques for solving Complementary Geometric Programming (GP). We also obtain a low-complexity approximate solution by solving an approximate problem with a differentiable objective function which is an upper bound on the original nondifferentiable one, and characterize the performance loss caused by the approximation. Finally, we present two information-theoretic converse bounds on the worst-case load and average load (under an arbitrary file popularity) in the general scenario, respectively. To the best of our knowledge, this is the first work that provides optimization-based decentralized coded caching schemes and information-theoretic converse bounds for the general scenario.

Today's deep neural networks (DNNs) are becoming deeper and wider because of increasing demand on the analysis quality and more and more complex applications to resolve. The wide and deep DNNs, however, require large amounts of resources (such as memory, storage, and I/O), significantly restricting their utilization on resource-constrained platforms. Although some DNN simplification methods (such as weight quantization) have been proposed to address this issue, they suffer from either low compression ratios or high compression errors, which may introduce an expensive fine-tuning overhead (i.e., a costly retraining process for the target inference accuracy). In this paper, we propose DeepSZ: an accuracy-loss expected neural network compression framework, which involves four key steps: network pruning, error bound assessment, optimization for error bound configuration, and compressed model generation, featuring a high compression ratio and low encoding time. The contribution is threefold. (1)We develop an adaptive approach to select the feasible error bounds for each layer. (2) We build a model to estimate the overall loss of inference accuracy based on the inference accuracy degradation caused by individual decompressed layers. (3) We develop an efficient optimization algorithm to determine the best-fit configuration of error bounds in order to maximize the compression ratio under the user-set inference accuracy constraint. Experiments show that DeepSZ can compress AlexNet and VGG-16 on the ImageNet dataset by a compression ratio of 46× and 116×, respectively, and compress LeNet-300-100 and LeNet-5 on the MNIST dataset by a compression ratio of 57× and 56×, respectively, with only up to 0.3% loss of inference accuracy. Compared with other state-of-the-art methods, DeepSZ can improve the compression ratio by up to 1.43×, the DNN encoding performance by up to 4.0× with four V100 GPUs, and the decoding performance by up to 6.2×.

The decentralized coded caching scheme of Maddah-Ali and Niesen for the shared link network achieves an order-optimal memory-load tradeoff when the file size goes to infinity. It is then successively shown by Shanmugam et al. that, in the practical operating regime where the file size is finite, such a scheme yields a much less attractive coded caching gain. In this paper, we focus on designing decentralized coded caching schemes that can achieve low worst case loads of the shared link when the file size is finite and maintain order-optimal memory-load tradeoffs when the file size grows to infinity. First, we propose a decentralized coded caching design framework for designing decentralized coded caching schemes that can achieve significantly lower worst case loads than Maddah-Ali-Niesen's decentralized coded caching scheme in the finite file size regime while maintaining order-optimal memory-load tradeoffs when the file size grows to infinity. Then, within the proposed framework, we propose a decentralized coded caching scheme, which is simple and tractable, and can achieve a low worst case load in both the finite and infinite file size regimes. We analyze the worst case load of the proposed scheme and show that it outperforms Maddah-Ali-Niesen's and Shanmugam et al.'s decentralized schemes in the finite file size regime when the number of users is not too small. We also analyze the asymptotic worst case load of the proposed scheme when the file size goes to infinity and show that the proposed scheme achieves an order-optimal memory-load tradeoff. Finally, we analytically characterize the behavior of the worst case coded caching gain of the proposed scheme as a function of the required file size when the file size is large.

Existing coded caching schemes fail to simultaneously achieve efficient content placement for non-uniform file popularity and efficient content delivery in the presence of common requests, and hence may not achieve desirable average load under a non-uniform, possibly very skewed, popularity distribution. In addition, existing coded caching schemes usually require the splitting of a file into a large number of subfiles, i.e., high subpacketization, and hence may cause huge implementation complexity. To address the above two challenges, we first present a class of centralized coded caching schemes consisting of a general content placement strategy specified by a file partition parameter, enabling efficient and flexible content placement, and a specific content delivery strategy, enabling load reduction by exploiting common requests of different users. Then we consider two cases, namely, the case without considering the subpacketization issue and the case considering the subpacke-tization issue. In the first case, we formulate the coded caching optimization problem over the considered class of schemes with N2Kvariables to minimize the average load under an arbitrary file popularity. Imposing some conditions on the file partition parameter, we transform the original optimization problem into a linear optimization problem with N(K + 1) variables under an arbitrary file popularity and a linear optimization problem with K +1 variables under the uniform file popularity. We also show that Yu et al.'s centralized coded caching scheme corresponds to an optimal solution of our problem. In the second case, taking into account the subpacketization issue, we first formulate the coded caching optimization problem over the considered class of schemes to minimize the average load under an arbitrary file popularity subject to a subpacketization constraint involving the ℓ0-norm. By imposing the same conditions and using an exact DC (difference of two convex functions) reformulation method, we convert the original problem with N2Kvariables into a simplified DC problem with N(K + 1) variables. Then, we use a DC algorithm to solve the simplified DC problem.

Recently, a new class of decentralized random coded caching schemes have received increasing interest, as they can achieve order-optimal memory-load tradeoff through decentralized content placement when the file size goes to infinity. However, most of these existing decentralized schemes may not provide enough coded- multicasting opportunities in the practical operating regime where the file size is limited. In this paper, we focus on the finite file size regime and propose a decentralized random coded caching scheme and a partially decentralized sequential coded caching scheme. These two schemes have different requirements on coordination in the content placement phase and can be applied to different scenarios. The content placement of the proposed schemes aims at ensuring abundant coded-multicasting opportunities in the content delivery phase when the file size is finite. We analyze the worst-case (over all possible requests) loads of our schemes and show that the sequential coded caching scheme outperforms the random coded caching scheme in the finite file size regime. Analytical results indicate that, when the file size grows to infinity, the proposed schemes achieve the same memory- load tradeoff as Maddah-Ali-Niesen's decentralized scheme, and hence are also order optimal. Numerical results show that the two proposed schemes outperform Maddah-Ali-Niesen's decentralized scheme when the file size is not very large.