CODAG: Characterizing and Optimizing Decompression Algorithms for GPUs

Data compression and decompression have become vital components of big-data applications to manage the exponential growth in the amount of data collected and stored. Furthermore, big-data applications have increasingly adopted GPUs due to their high compute throughput and memory bandwidth. Prior works presume that decompression is memory-bound and have dedicated most of the GPU's threads to data movement and adopted complex software techniques to hide memory latency for reading compressed data and writing uncompressed data. This paper shows that these techniques lead to poor GPU resource utilization as most threads end up waiting for the few decoding threads, exposing compute and synchronization latencies. Based on this observation, we propose CODAG, a novel and simple kernel architecture for high throughput decompression on GPUs. CODAG eliminates the use of specialized groups of threads, frees up compute resources to increase the number of parallel decompression streams, and leverages the ample compute activities and the GPU's hardware scheduler to tolerate synchronization, compute, and memory latencies. Furthermore, CODAG provides a framework for users to easily incorporate new decompression algorithms without being burdened with implementing complex optimizations to hide memory latency. We validate our proposed architecture with three different encoding techniques, RLE v1, RLE v2, and Deflate, and a wide range of large datasets from different domains. We show that CODAG provides 13.46x, 5.69x, and 1.18x speed up for RLE v1, RLE v2, and Deflate, respectively, when compared to the state-of-the-art decompressors from NVIDIA RAPIDS

Lossless Compression for Semantic Textures

A semantic texture overlays semantic labels over an image to indicate the type of texture represented by each region of the image. Traditional lossy compression works well for color textures, but not for semantic textures. This disclosure describes lossless compression techniques to compress semantic textures, thereby reducing the memory occupied by semantic textures. The techniques leverage the observation that semantic textures, unlike color textures, are highly structured with large blocks of common values. The techniques enable high-speed access to detailed rendering and resolution in real-time computer graphics. They also enable additional textures or texture resolution, enhancing the detail and realism of rendering

Optimizing Apple Lossless Audio Codec Algorithm using NVIDIA CUDA Architecture

As majority of the compression algorithms are implementations for CPU architecture, the primary focus of our work was to exploit the opportunities of GPU parallelism in audio compression. This paper presents an implementation of Apple Lossless Audio Codec (ALAC) algorithm by using NVIDIA GPUs Compute Unified Device Architecture (CUDA) Framework. The core idea was to identify the areas where data parallelism could be applied and parallel programming model CUDA could be used to execute the identified parallel components on Single Instruction Multiple Thread (SIMT) model of CUDA. The dataset was retrieved from European Broadcasting Union, Sound Quality Assessment Material (SQAM). Faster execution of the algorithm led to execution time reduction when applied to audio coding for large audios. This paper also presents the reduction of power usage due to running the parallel components on GPU. Experimental results reveal that we achieve about 80-90% speedup through CUDA on the identified components over its CPU implementation while saving CPU power consumption

GPU-Acceleration of In-Memory Data Analytics

Hardware advances strongly influence the database system design. The flattening speed of CPU cores makes many-core accelerators, such as GPUs, a vital alternative to explore for processing the ever-increasing amounts of data. GPUs have a significantly higher degree of parallelism than multi-core CPUs but their cores are simpler. As a result, they do not face the power constraints limiting the parallelism of CPUs. Their trade-off, however, is the increased implementation complexity. This thesis adapts and redesigns data analytics operators to better exploit the GPU special memory and threading model. Due to the increasing memory capacity and also the user's need for fast interaction with the data, we focus on in-memory analytics. Our techniques span different steps of the data processing pipeline: (1) Data preprocessing, (2) Query compilation, and (3) Algorithmic optimization of the operators. Our data preprocessing techniques adapt the data layout for numeric and string columns to maximize the achieved GPU memory bandwidth. Our query compilation techniques compute the optimal execution plan for conjunctive filters. We formulate \textit{memory divergence} for string matching algorithms and suggest how to eliminate it. Finally, we parallelize decompression algorithms in our compression framework \textit{Gompresso} to fit more data into the limited GPU memory. Gompresso achieves high speed-ups on GPUs over multi-core CPU state-of-the-art libraries and is suitable for any massively parallel processor

Ultrafast Error-Bounded Lossy Compression for Scientific Datasets

Today\u27s scientific high-performance computing applications and advanced instruments are producing vast volumes of data across a wide range of domains, which impose a serious burden on data transfer and storage. Error-bounded lossy compression has been developed and widely used in the scientific community because it not only can significantly reduce the data volumes but also can strictly control the data distortion based on the user-specified error bound. Existing lossy compressors, however, cannot offer ultrafast compression speed, which is highly demanded by numerous applications or use cases (such as in-memory compression and online instrument data compression). In this paper, we propose a novel ultrafast error-bounded lossy compressor that can obtain fairly high compression performance on both CPUs and GPUs and with reasonably high compression ratios. The key contributions are threefold. (1) We propose a generic error-bounded lossy compression framework - -called SZx - -that achieves ultrafast performance through its novel design comprising only lightweight operations such as bitwise and addition/subtraction operations, while still keeping a high compression ratio. (2) We implement SZx on both CPUs and GPUs and optimize the performance according to their architectures. (3) We perform a comprehensive evaluation with six real-world production-level scientific datasets on both CPUs and GPUs. Experiments show that SZx is 2∼16x faster than the second-fastest existing error-bounded lossy compressor (either SZ or ZFP) on CPUs and GPUs, with respect to both compression and decompression

GST: GPU-decodable supercompressed textures

Modern GPUs supporting compressed textures allow interactive application developers to save scarce GPU resources such as VRAM and bandwidth. Compressed textures use fixed compression ratios whose lossy representations are significantly poorer quality than traditional image compression formats such as JPEG. We present a new method in the class of supercompressed textures that provides an additional layer of compression to already compressed textures. Our texture representation is designed for endpoint compressed formats such as DXT and PVRTC and decoding on commodity GPUs. We apply our algorithm to commonly used formats by separating their representation into two parts that are processed independently and then entropy encoded. Our method preserves the CPU-GPU bandwidth during the decoding phase and exploits the parallelism of GPUs to provide up to 3X faster decode compared to prior texture supercompression algorithms. Along with the gains in decoding speed, our method maintains both the compression size and quality of current state of the art texture representations

High throughput image compression and decompression on GPUs

Diese Arbeit befasst sich mit der Entwicklung eines GPU-freundlichen, intra-only, Wavelet-basierten Videokompressionsverfahrens mit hohem Durchsatz, das für visuell verlustfreie Anwendungen optimiert ist. Ausgehend von der Beobachtung, dass der JPEG 2000 Entropie-Kodierer ein Flaschenhals ist, werden verschiedene algorithmische Änderungen vorgeschlagen und bewertet. Zunächst wird der JPEG 2000 Selective Arithmetic Coding Mode auf der GPU realisiert, wobei sich die Erhöhung des Durchsatzes hierdurch als begrenzt zeigt. Stattdessen werden zwei nicht standard-kompatible Änderungen vorgeschlagen, die (1) jede Bitebebene in nur einem einzelnen Pass verarbeiten (Single-Pass-Modus) und (2) einen echten Rohcodierungsmodus einführen, der sample-weise parallelisierbar ist und keine aufwendige Kontextmodellierung erfordert. Als nächstes wird ein alternativer Entropiekodierer aus der Literatur, der Bitplane Coder with Parallel Coefficient Processing (BPC-PaCo), evaluiert. Er gibt Signaladaptivität zu Gunsten von höherer Parallelität auf und daher wird hier untersucht und gezeigt, dass ein aus verschiedensten Testsequenzen gemitteltes statisches Wahrscheinlichkeitsmodell eine kompetitive Kompressionseffizienz erreicht. Es wird zudem eine Kombination von BPC-PaCo mit dem Single-Pass-Modus vorgeschlagen, der den Speedup gegenüber dem JPEG 2000 Entropiekodierer von 2,15x (BPC-PaCo mit zwei Pässen) auf 2,6x (BPC-PaCo mit Single-Pass-Modus) erhöht auf Kosten eines um 0,3 dB auf 1,0 dB erhöhten Spitzen-Signal-Rausch-Verhältnis (PSNR). Weiter wird ein paralleler Algorithmus zur Post-Compression Ratenkontrolle vorgestellt sowie eine parallele Codestream-Erstellung auf der GPU. Es wird weiterhin ein theoretisches Laufzeitmodell formuliert, das es durch Benchmarking von einer GPU ermöglicht die Laufzeit einer Routine auf einer anderen GPU vorherzusagen. Schließlich wird der erste JPEG XS GPU Decoder vorgestellt und evaluiert. JPEG XS wurde als Low Complexity Codec konzipiert und forderte erstmals explizit GPU-Freundlichkeit bereits im Call for Proposals. Ab Bitraten über 1 bpp ist der Decoder etwa 2x schneller im Vergleich zu JPEG 2000 und 1,5x schneller als der schnellste hier vorgestellte Entropiekodierer (BPC-PaCo mit Single-Pass-Modus). Mit einer GeForce GTX 1080 wird ein Decoder Durchsatz von rund 200 fps für eine UHD-4:4:4-Sequenz erreicht.This work investigates possibilities to create a high throughput, GPU-friendly, intra-only, Wavelet-based video compression algorithm optimized for visually lossless applications. Addressing the key observation that JPEG 2000’s entropy coder is a bottleneck and might be overly complex for a high bit rate scenario, various algorithmic alterations are proposed. First, JPEG 2000’s Selective Arithmetic Coding mode is realized on the GPU, but the gains in terms of an increased throughput are shown to be limited. Instead, two independent alterations not compliant to the standard are proposed, that (1) give up the concept of intra-bit plane truncation points and (2) introduce a true raw-coding mode that is fully parallelizable and does not require any context modeling. Next, an alternative block coder from the literature, the Bitplane Coder with Parallel Coefficient Processing (BPC-PaCo), is evaluated. Since it trades signal adaptiveness for increased parallelism, it is shown here how a stationary probability model averaged from a set of test sequences yields competitive compression efficiency. A combination of BPC-PaCo with the single-pass mode is proposed and shown to increase the speedup with respect to the original JPEG 2000 entropy coder from 2.15x (BPC-PaCo with two passes) to 2.6x (proposed BPC-PaCo with single-pass mode) at the marginal cost of increasing the PSNR penalty by 0.3 dB to at most 1 dB. Furthermore, a parallel algorithm is presented that determines the optimal code block bit stream truncation points (given an available bit rate budget) and builds the entire code stream on the GPU, reducing the amount of data that has to be transferred back into host memory to a minimum. A theoretical runtime model is formulated that allows, based on benchmarking results on one GPU, to predict the runtime of a kernel on another GPU. Lastly, the first ever JPEG XS GPU-decoder realization is presented. JPEG XS was designed to be a low complexity codec and for the first time explicitly demanded GPU-friendliness already in the call for proposals. Starting at bit rates above 1 bpp, the decoder is around 2x faster compared to the original JPEG 2000 and 1.5x faster compared to JPEG 2000 with the fastest evaluated entropy coder (BPC-PaCo with single-pass mode). With a GeForce GTX 1080, a decoding throughput of around 200 fps is achieved for a UHD 4:4:4 sequence

GPU-oriented architecture for an end-to-end image/video codec based on JPEG2000

Modern image and video compression standards employ computationally intensive algorithms that provide advanced features to the coding system. Current standards often need to be implemented in hardware or using expensive solutions to meet the real-time requirements of some environments. Contrarily to this trend, this paper proposes an end-to-end codec architecture running on inexpensive Graphics Processing Units (GPUs) that is based on, though not compatible with, the JPEG2000 international standard for image and video compression. When executed in a commodity Nvidia GPU, it achieves real time processing of 12K video. The proposed S/W architecture utilizes four CUDA kernels that minimize memory transfers, use registers instead of shared memory, and employ a double-buffer strategy to optimize the streaming of data. The analysis of throughput indicates that the proposed codec yields results at least 10× superior on average to those achieved with JPEG2000 implementations devised for CPUs, and approximately 4× superior to those achieved with hardwired solutions of the HEVC/H.265 video compression standard