Soft error, namely silent corruption of signal or datum in a computer system, cannot be caverlierly ignored as compute and communication density grow exponentially. Soft error detection has been studied in the context of enterprise computing, high-performance computing and more recently in convolutional neural networks related to autonomous driving.Deep learning recommendation systems (DLRMs) have by now become ubiquitous and serve billions of users per day. Nevertheless, DLRM-specific soft error detection methods are hitherto missing. To fill the gap, this paper presents the first set of soft-error detection methods for low-precision quantized-arithmetic operators in DLRM including general matrix multiplication (GEMM) and EmbeddingBag. A practical method must detect error and do so with low overhead lest reduced inference speed degrades user experience. Exploiting the characteristics of both quantized arithmetic and the operators, we achieved more than 95% detection accuracy for GEMM with an overhead below 20%. For EmbeddingBag, we achieved 99% effectiveness in significant-bit-flips detection with less than 10% of false positives, while keeping overhead below 26%.

In the past decades, various lossy compressors have been studied broadly due to the ever-increasing volume of data being produced by today’s scientific applications. SZ has been one of the best error-bounded lossy compressors ever raised, and it has a flexible framework that includes four adjustable steps: prediction, quantization, variable-length encoding, and lossless compression. In this paper, we improve the lossy compression performances of the SZ compression model by exploring different existing lossless compression techniques using the Squash data compression benchmark. Specifically, we first characterize the bytes outputted by the first three steps in SZ, then we investigate the best lossless compressor with different datasets and different error bounds. We perform our exploration by testing 8 widely used lossless compressors under different configurations together with SZ over five well-known scientific simulation datasets. Our experiments show that adopting the best-fit lossless compressor selected based on our analysis can improve the overall compression speed by up to 40% compared to the previous lossless compression technique used in SZ with the comparable quality of reconstructed data.

Efficient error-controlled lossy compressors are becoming critical to the success of today's large-scale scientific applications because of the ever-increasing volume of data produced by the applications. In the past decade, many lossless and lossy compressors have been developed with distinct design principles for different scientific datasets in largely diverse scientific domains. In order to support researchers and users assessing and comparing compressors in a fair and convenient way, we establish a standard compression assessment benchmark - Scientific Data Reduction Benchmark (SDRBench)1. SDRBench contains a vast variety of real-world scientific datasets across different domains, summarizes several critical compression quality evaluation metrics, and integrates many state-of-the-art lossy and lossless compressors. We demonstrate evaluation results using SDRBench and summarize six valuable takeaways that are helpful to the in-depth understanding of lossy compressors.

Today's N-body simulations are producing extremely large amounts of data. The Hardware/Hybrid Accelerated Cosmology Code (HACC), for example, may simulate trillions of particles, producing tens of petabytes of data to store in a parallel file system, according to the HACC users. In this paper, we design and implement an efficient, in situ error-bounded lossy compressor to significantly reduce the data size for N-body simulations. Not only can our compressor save significant storage space for N-body simulation researchers, but it can also improve the I/O performance considerably with limited memory and computation overhead. Our contribution is threefold. (1) We propose an efficient data compression model by leveraging the consecutiveness of the cosmological data in both space and time dimensions as well as the physical correlation across different fields. (2) We propose a lightweight, efficient alignment mechanism to align the disordered particles across adjacent snapshots in the simulation, which is a fundamental step in the whole compression procedure. We also optimize the compression quality by exploring best-fit data prediction strategies and optimizing the frequencies of the space-based compression vs. time-based compression. (3) We evaluate our compressor using both a cosmological simulation package and molecular dynamics simulation data-two major categories in the N-body simulation domain. Experiments show that under the same distortion of data, our solution produces up to 43% higher compression ratios on the velocity field and up to 300% higher on the position field than do other state-of-the-art compressors (including SZ, ZFP, NUMARCK, and decimation). With our compressor, the overall I/O time on HACC data is reduced by up to 20% compared with the second-best compressor.

Today's scientific simulations require a significant reduction of the data size because of extremely large volumes of data they produce and the limitation of storage bandwidth and space. If the compression is set to reach a high compression ratio, however, the reconstructed data are often distorted too much to tolerate. In this paper, we explore a new compression strategy that can effectively control the data distortion when significantly reducing the data size. The contribution is threefold. (1) We propose an adaptive compression framework to select either our improved Lorenzo prediction method or our optimized linear regression method dynamically in different regions of the dataset. (2) We explore how to select them accurately based on the data features in each block to obtain the best compression quality. (3) We analyze the effectiveness of our solution in details using four real-world scientific datasets with 100+ fields. Evaluation results confirm that our new adaptive solution can significantly improve the rate distortion for the lossy compression with fairly high compression ratios. The compression ratio of our compressor is 1.5X~8X as high as that of two other leading lossy compressors (SZ and ZFP) with the same peak single-to-noise ratio (PSNR), in the high-compression cases. Parallel experiments with 8,192 cores and 24 TB of data shows that our solution obtains 1.86X dumping performance and 1.95X loading performance compared with the second-best lossy compressor, respectively.