17 research outputs found
Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP
With ever-increasing volumes of scientific data produced by HPC applications,
significantly reducing data size is critical because of limited capacity of
storage space and potential bottlenecks on I/O or networks in writing/reading
or transferring data. SZ and ZFP are the two leading lossy compressors
available to compress scientific data sets. However, their performance is not
consistent across different data sets and across different fields of some data
sets: for some fields SZ provides better compression performance, while other
fields are better compressed with ZFP. This situation raises the need for an
automatic online (during compression) selection between SZ and ZFP, with a
minimal overhead. In this paper, the automatic selection optimizes the
rate-distortion, an important statistical quality metric based on the
signal-to-noise ratio. To optimize for rate-distortion, we investigate the
principles of SZ and ZFP. We then propose an efficient online, low-overhead
selection algorithm that predicts the compression quality accurately for two
compressors in early processing stages and selects the best-fit compressor for
each data field. We implement the selection algorithm into an open-source
library, and we evaluate the effectiveness of our proposed solution against
plain SZ and ZFP in a parallel environment with 1,024 cores. Evaluation results
on three data sets representing about 100 fields show that our selection
algorithm improves the compression ratio up to 70% with the same level of data
distortion because of very accurate selection (around 99%) of the best-fit
compressor, with little overhead (less than 7% in the experiments).Comment: 14 pages, 9 figures, first revisio
Dynamic Quality Metric Oriented Error-bounded Lossy Compression for Scientific Datasets
With the ever-increasing execution scale of high performance computing (HPC)
applications, vast amounts of data are being produced by scientific research
every day. Error-bounded lossy compression has been considered a very promising
solution to address the big-data issue for scientific applications because it
can significantly reduce the data volume with low time cost meanwhile allowing
users to control the compression errors with a specified error bound. The
existing error-bounded lossy compressors, however, are all developed based on
inflexible designs or compression pipelines, which cannot adapt to diverse
compression quality requirements/metrics favored by different application
users. In this paper, we propose a novel dynamic quality metric oriented
error-bounded lossy compression framework, namely QoZ. The detailed
contribution is three-fold. (1) We design a novel highly-parameterized
multi-level interpolation-based data predictor, which can significantly improve
the overall compression quality with the same compressed size. (2) We design
the error-bounded lossy compression framework QoZ based on the adaptive
predictor, which can auto-tune the critical parameters and optimize the
compression result according to user-specified quality metrics during online
compression. (3) We evaluate QoZ carefully by comparing its compression quality
with multiple state-of-the-arts on various real-world scientific application
datasets. Experiments show that, compared with the second-best lossy
compressor, QoZ can achieve up to 70% compression ratio improvement under the
same error bound, up to 150% compression ratio improvement under the same PSNR,
or up to 270% compression ratio improvement under the same SSIM
Recommended from our members
Toward Resilience and Data Reduction in Exascale Scientific Computing
Because of the ever-increasing execution scale, reliability and data management are becoming more and more important for scientific applications. On the one hand, exascale systems are anticipated to be more susceptible to soft errors ,e.g. silent data corruptions, due to the reduction in the size of transistors and the increase of the number of components. These errors will lead to corrupted results without warning, making the output of the computation untrustable. On the other hand, large volumes of highly variable data are produced by scientific computing with high velocity on exascale systems or advanced instruments, and the I/O time on storing these data is prohibitive due to the I/O bottleneck in parallel file systems. In this work, we leverage algorithm-based fault tolerance (ABFT) and error-bound lossy compression to tackle the two problems, in order to support efficient scientific computing on exascale systems.We propose an efficient fault tolerant scheme to tolerant soft errors in Fast Fourier Transform (FFT), one of the most important computation kernels widely used in scientific computing. Traditional redundancy approaches will at least double the execution time or resources, limiting the usage in practice because of the large overhead. Previous works on offline ABFT algorithms for FFT mitigate this problem by providing resilient FFT with lower overhead, but these algorithms fail to make progress in vulnerable environments with high error rates because they can only detect and correct errors after the whole computation finishes. We propose an online ABFT scheme for large-scale FFT inspired by the divide-and-conquer nature of the FFT computation. We devise fault tolerant schemes for both computational and memory errors in FFT, with both serial and parallel optimizations. Experimental results demonstrate that the proposed approach provides more timely error detection and recovery as well as better fault coverage with less overhead, compared to the offline ABFT algorithm.To alleviate the I/O bottleneck in the parallel file systems, we work on a prediction-based error-bounded lossy compressor to significantly reduce the size of scientific datasets while retaining the accuracy of the decompressed data, with adaptive prediction algorithms and compression models. We first propose a regression-based predictor for better prediction accuracy than traditional approaches under large error bounds, followed by an adaptive algorithm that dynamically selects between the traditional Lorenzo predictor and the proposed regression-based predictor, leading to very high compression ratios with little visual distortion. We further unify the prediction-based model and transform-baed model by using transform-based compressors as a predictor, with novel optimizations toward efficient coefficient encoding for both the two models. The proposed adaptive multi-algorithm design provides better compression ratios given the same distortion, significantly reducing storage requirements and I/O time.We further adapt the compression algorithms and compressors to different requirements and/or objectives in realistic scenarios. We leverage a logarithmic transform to precondition the data, which turns a relative-error-bound compression problem into an absolute-error-bound compression problem. This transform aligns two different error requirements while improving the compression quality, efficiently reducing the workload for compressor design. We also correlate the compression algorithm with system information to achieve better I/O performance compared to traditional single compressor deployment. These studies further improve the efficiency of lossy compression from the perspective of efficient I/O in the context of scientific simulation, making scientific applications running on exascale systems more efficient
Approachable Error Bounded Lossy Compression
Compression is commonly used in HPC applications to move and store data. Traditional lossless compression, however, does not provide adequate compression of floating point data often found in scientific codes. Recently, researchers and scientists have turned to lossy compression techniques that approximate the original data rather than reproduce it in order to achieve desired levels of compression. Typical lossy compressors do not bound the errors introduced into the data, leading to the development of error bounded lossy compressors (EBLC). These tools provide the desired levels of compression as mathematical guarantees on the errors introduced. However, the current state of EBLC leaves much to be desired. The existing EBLC all have different interfaces requiring codes to be changed to adopt new techniques; EBLC have many more configuration options than their predecessors, making them more difficult to use; and EBLC typically bound quantities like point wise errors rather than higher level metrics such as spectra, p-values, or test statistics that scientists typically use. My dissertation aims to provide a uniform interface to compression and to develop tools to allow application scientists to understand and apply EBLC. This dissertation proposal presents three groups of work: LibPressio, a standard interface for compression and analysis; FRaZ/LibPressio-Opt frameworks for the automated configuration of compressors using LibPressio; and work on tools for analyzing errors in particular domains
CEAZ: Accelerating Parallel I/O via Hardware-Algorithm Co-Design of Efficient and Adaptive Lossy Compression
As supercomputers 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
of efficient and adaptive lossy compressor for scientific data on FPGAs (called
CEAZ) to accelerate parallel I/O. Our contribution is fourfold: (1) 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). (2) 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.
(3) We develop an efficient compression pipeline by adopting cuSZ's
dual-quantization algorithm to our hardware use case. (4) We evaluate CEAZ on
five real-world datasets with both a single FPGA board and 128 nodes from
Bridges-2 supercomputer. Experiments show that CEAZ outperforms the second-best
FPGA-based lossy compressor by 2X of throughput and 9.6X of compression ratio.
It also improves MPI_File_write and MPI_Gather throughputs by up to 25.8X and
24.8X, respectively.Comment: 14 pages, 17 figures, 8 table