Directly performing operations on compressed data has been proven to be a big success facing Big Data problems in modern data management systems. These systems have demonstrated significant compression benefits and performance improvement for data analytics applications. However, current systems only focus on data queries, while a complete Big Data system must support both data query and data manipulation. To solve this problem, we develop CompressDB, which is a new storage engine that can support data processing for databases without decompression. CompressDB has the following advantages. First, CompressDB utilizes context-free grammar to compress data, and supports both data query and data manipulation. Second, for adaptability, we integrate CompressDB to file systems so that a wide range of databases can directly use CompressDB without any change. Third, we enable operation pushdown to storage so that we can perform data query and manipulation in storage systems without bringing large data to memory for high efficiency. We validate the efficacy of CompressDB supporting various kinds of database systems, including SQLite, MySQL, LevelDB, MongoDB, ClickHouse, and Neo4j. We evaluate our method using seven real-world datasets with various lengths, structures, and content in both single node and cluster environments. Experiments show that CompressDB achieves 40% throughput improvement and 44% latency reduction, along with 1.75 compression ratio on average.

With the development of the architectures and the growth of AIoT application requirements, data processing on edge has become popular. Neural network inference is widely employed for data analytics on edge devices. This paper extensively explores neural network inference on integrated edge devices and proposes EdgeNN, the first neural network inference solution on CPU-GPU integrated edge devices. EdgeNN has three novel characteristics. First, EdgeNN can adaptively utilize the unified physical memory and conduct the zero-copy optimization. Second, EdgeNN involves a novel inference-targeted inter- and intra-kernel CPU-GPU hybrid execution approach, which co-runs the CPU with the GPU to fully utilize the edge device’s computing resources. Third, EdgeNN adopts a fine-grained adaptive inference tuning approach, which can divide the complicated inference structure into sub-tasks mapped to the CPU and the GPU. Experiments show that on six popular neural network inference tasks, EdgeNN brings an average of 3.97×, 3.12×, and 8.80× speedups to inference on the CPU of the integrated device, inference on a mobile phone CPU, and inference on an edge CPU device. Additionally, it achieves 22.02% time benefits to the direct execution of the original programs. Specifically, 9.93% comes from better utilization of unified memory, and 10.76% comes from CPU-GPU hybrid execution. Besides, EdgeNN can deliver 29.14× and 5.70× higher energy efficiency than the edge CPU and the discrete GPU, respectively. We have made EdgeNN available at https://github.com/ChenyangZhang-cs/EdgeNN.

In modern data management systems, directly performing operations on compressed data has been proven to be a big success facing big data problems. These systems have demonstrated significant compression benefits and performance improvement for data analytics applications. However, current systems only focus on data queries, while a complete big data system must support both data query and data manipulation.

Deep learning has been one of the trendiest research topics. However, as data quantities rise exponentially, training large neural networks can become prohibitively expensive with billions of parameters. Fortunately, recent research has discovered that not all of the computations in traditional network training are necessary. By selectively sparsifying the majority of the neurons during training, we can still obtain acceptable accuracy. SLIDE, a C++ OpenMP-based sub-linear deep learning engine, has been developed in this situation. SLIDE uses the algorithm of locality-sensitive hashing (LSH) to query neurons with high activation in sub-linear time. It achieves a remarkable speedup in training large fully-connected networks by making use of the network sparsity as well as multi-core parallelism. However, SLIDE is limited to CPUs, ignoring the popular GPU devices with greater parallel potential and computational capability. In this paper, we propose G-SLIDE, a GPU-based sub-linear deep learning engine, which combines the benefits of SLIDEs adaptive sparsification algorithms with GPUs high performance. The main challenges in developing G-SLIDE are efficiently using LSH to sparsify networks and training the special sparse neural networks on the GPU. To address these challenges, we propose several novel solutions, such as specific data formats and appropriate workload partitioning for threads to fully utilize the GPU resources. We evaluate G-SLIDE on two extremely sparse datasets with a 2080 Ti GPU, and the results demonstrate that for the time of one training epoch, G-SLIDE can achieve more than 16.4 speedup over SLIDE on a 32-core/64-thread CPU. Furthermore, on the same platform, G-SLIDE can earn an average of 16.2 speedup over TensorFlow-GPU and 30.8 speedup over TensorFlow-CPU.

Machine learning is widely used in our daily lives. Large amounts of data have been continuously produced and transmitted to the cloud for model training and data processing, which raises a problem: how to preserve the security of the data. Recently, a secure machine learning system named SecureML has been proposed to solve this issue using two-party computation. However, due to the excessive computation expenses of two-party computation, the secure machine learning is about 2× slower than the original machine learning methods. Previous work on secure machine learning mostly focused on novel protocols or improving accuracy, while the performance metric has been ignored. In this article, we propose a GPU-based framework ParSecureML to improve the performance of secure machine learning algorithms based on two-party computation. The main challenges of developing ParSecureML lie in the complex computation patterns, frequent intra-node data transmission between CPU and GPU, and complicated inter-node data dependence. To handle these challenges, we propose a series of novel solutions, including profiling-guided adaptive GPU utilization, fine-grained double pipeline for intra-node CPU-GPU cooperation, and compressed transmission for inter-node communication. Moreover, we integrate architecture specific optimizations, such as Tensor Cores, into ParSecureML. As far as we know, this is the first GPU-based secure machine learning framework. Compared to the state-of-the-art framework, ParSecureML achieves an average of 33.8× speedup. ParSecureML can also be applied to inferences, which achieves 31.7× speedup on average.

Utilizing heterogeneous accelerators, especially GPUs, to accelerate machine learning tasks has shown to be a great success in recent years. GPUs bring huge performance improvements to machine learning and greatly promote the widespread adoption of machine learning. However, the discrete CPU-GPU architecture design with high PCIe transmission overhead decreases the GPU computing benefits in machine learning training tasks. To overcome such limitations, hardware vendors release CPU-GPU integrated architectures with shared unified memory. In this article, we design a benchmark suite for machine learning training on CPU-GPU integrated architectures, called iMLBench, covering a wide range of machine learning applications and kernels. We mainly explore two features on integrated architectures: 1) zero-copy, which means that the PCIe overhead has been eliminated for machine learning tasks and 2) co-running, which means that the CPU and the GPU co-run together to process a single machine learning task. Our experimental results on iMLBench show that the integrated architecture brings an average 7.1× performance improvement over the original implementations. Specifically, the zero-copy design brings 4.65× performance improvement, and co-running brings 1.78× improvement. Moreover, integrated architectures exhibit promising results from both performance-per-dollar and energy perspectives, achieving 6.50× performance-price ratio while 4.06× energy efficiency over discrete GPUs. The benchmark is open-sourced at https://github.com/ChenyangZhang-cs/iMLBench.

Exploring the sharing opportunities among multiple stream queries is crucial for high-performance stream processing. Modern stream processing necessitates accelerating multiple queries by utilizing heterogeneous coprocessors, such as GPUs, and this has shown to be an effective method. Emerging CPU-GPU integrated architectures 6integrate CPU and GPU on the same chip and eliminate PCI-e bandwidth bottleneck. Such a novel architecture provides new opportunities for improving multi-query performance in stream processing but has not been fully explored by existing systems. We introduce a stream processing engine, called FineStream, for efficient multi-query window-based stream processing on CPU-GPU integrated architectures. FineStream's key contribution is a novel fine-grained workload scheduling mechanism between CPU and GPU to take advantage of both architectures. Particularly, FineStream is able to efficiently handle multiple queries in both static and dynamic streams. Our experimental results show that 1) on integrated architectures, FineStream achieves an average 52 percent throughput improvement and 36 percent lower latency over the state-of-the-art stream processing engine; 2) compared to the coarse-grained strategy of applying different devices for multiple queries, FineStream achieves 32 percent throughput improvement; 3) compared to the stream processing engine on the discrete architecture, FineStream on the integrated architecture achieves 10.4× price-throughput ratio, 1.8× energy efficiency, and can enjoy lower latency benefits.

Machine learning has been widely used in our daily lives. Large amounts of data have been continuously produced and transmitted to the cloud for model training and data processing, which raises a problem: how to preserve the security of the data. Recently, a secure machine learning system named SecureML has been proposed to solve this issue using two-party computation. However, due to the excessive computation expenses of two-party computation, the secure machine learning is about 2x slower than the original machine learning methods. Previous work on secure machine learning mostly focused on novel protocols or improving accuracy, while the performance metric has been ignored. In this paper, we propose a GPU-based framework ParSecureML to improve the performance of secure machine learning algorithms based on two-party computation. The main challenges of developing ParSecureML lie in the complex computation patterns, frequent intra-node data transmission between CPU and GPU, and complicated inter-node data dependence. To handle these challenges, we propose a series of novel solutions, including profiling-guided adaptive GPU utilization, fine-grained double pipeline for intra-node CPU-GPU cooperation, and compressed transmission for inter-node communication. As far as we know, this is the first GPU-based secure machine learning framework. Compared to the state-of-the-art framework, ParSecureML achieves an average of 32.2x speedup. ParSecureML can be downloaded from https://github.com/ZhengChenCS/ParSecureML.