1,184 papers · page 10 of 60
Multiversioning is widely used in databases, transactional memory, and concurrent data structures. It can be used to support read-only transactions that appear atomic in the presence of concurrent update operations. Any system that maintains multiple versions of each object needs…
LU factorization for sparse matrices is an important computing step for many engineering and scientific problems such as circuit simulation. There have been many efforts toward parallelizing and scaling this algorithm, which include the recent efforts targeting the GPUs. However,…
Tucker decomposition is one of the SOTA CNN model compression techniques. However, unlike the FLOPs reduction, we observe very limited inference time reduction with Tucker-compressed models using existing GPU software such as cuDNN. To this end, we propose an efficient end-to-end…
The emergence of heterogeneous memory (HM) provides a cost-effective and high-performance solution to memory-consuming HPC applications. Deciding the placement of data objects on HM is critical for high performance. We reveal a performance problem related to data placement on HM.…
Random walk serves as a powerful tool in dealing with large-scale graphs, reducing data size while preserving structural information. Unfortunately, existing system frameworks all focus on the execution of a single walker task in serial. We propose CoWalker, a high-throughput GPU…
Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse kernel. Numerous methods have been developed to accelerate SpMV. However, no single method consistently gives the highest performance across a wide range of matrices. For this reason, a performance prediction model…
Symmetric eigenvalue decomposition (EVD) is a fundamental analytic and numerical tool used in many scientific areas. The state-of-the-art algorithm in terms of performance is typically the two-stage tridiagonalization method. The first stage in the two-stage tridiagonalization is…
Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are …
As the size of deep learning models gets larger and larger, training takes longer time and more resources, making fault tolerance critical. Existing state-of-the-art methods like Check-Freq and Elastic Horovod need to back up a copy of the model state in memory, which is costly f…
Neighbor search is of fundamental importance to many engineering and science fields such as physics simulation and computer graphics. This paper proposes to formulate neighbor search as a ray tracing problem and leverage the dedicated ray tracing hardware in recent GPUs for accel…
The concurrency literature presents a number of approaches for building non-blocking, FIFO, multiple-producer and multiple-consumer (MPMC) queues. However, existing wait-free queues are either not very scalable or suffer from potentially unbounded memory usage. We present a wait-…
We present KumQuat, a system for automatically generating data-parallel implementations of Unix shell commands and pipelines. The generated parallel versions split input streams, execute multiple instantiations of the original pipeline commands to process the splits in parallel, …
To maximize the performance of concurrent data structures, researchers have often turned to highly complex fine-grained techniques, resulting in efficient and elegant algorithms, which can however be often difficult to understand and prove correct. While simpler techniques exist,…
Communication overhead is one of the major obstacles to train large deep learning models at scale. Gradient sparsification is a promising technique to reduce the communication volume. However, it is very challenging to obtain real performance improvement because of (1) the diffic…
Modern computing platforms tend to deploy multiple GPUs on a single node to boost performance. GPUs have large computing capacities and are an expensive resource. Increasing their utilization without causing performance degradation of individual workloads is an important and chal…
The Problem-Based Benchmark Suite (PBBS) is a set of benchmark problems designed for comparing algorithms, implementations and platforms. For each problem, the suite defines the problem in terms of the input-output relationship, and supplies a set of input instances along with in…
This paper presents a generic approach for deriving detectably recoverable implementations of many widely-used concurrent data structures. Such implementations are appealing for emerging systems featuring byte-addressable non-volatile main memory (NVMM), whose persistence allows …
This paper presents a new and practical approach to lock-free locks based on helping, which allows the user to write code using fine-grained locks, but run it in a lock-free manner. Although lock-free locks have been suggested in the past, they are widely viewed as impractical, h…
Graph processing, especially high-performance graph traversal, plays a more and more important role in data analytics. The successor of Sunway TaihuLight, New Sunway, is equipped with nearly 10 PB memory and over 40 million cores, which brings the opportunity to process hundreds …
This work proposes a framework called FuSy that analyzes the data dependence graphs (DAGs) of two sparse kernels and creates an efficient schedule to execute the kernels in combination. Sparse kernels are frequently used in scientific codes and in machine learning algorithms and …