26,098 papers · page 80 of 1,305
Scientific images play a crucial role in many experimental sciences; however, the large volumes of data generated present significant challenges. Effective image compression must be fast, achieve high compression ratios, and preserve critical domain-specific features. Existing co…
The carbon and water footprint of large-scale computing systems poses serious environmental sustainability risks. In this study, we discover that, unfortunately, carbon and water sustainability are at odds with each other - and, optimizing one alone hurts the other. Toward that g…
MCS lock and its variants provide scalability on many-core architectures, using lists of lock requests to reduce access contention on the mutex data. Most recent variants have adopted a two-stage design, allowing requests to be allocated from stack memory rather than heap. Howeve…
Cloud service providers heavily colocate high-priority, latency sensitive (LS), and low-priority, best-effort (BE) DNN inference services on the same GPU to improve resource utilization in data centers. Among the critical shared GPU resources, there has been very limited analysis…
Graph reordering is an effective technique for improving the access locality of graph processing. However, existing methods often overlook the data access locality among concurrently activated vertices (a.k.a. frontiers). These vertices, while lacking direct connections or shared…
In this work, we present theoretically and practically efficient implementations of Big Atomics, i.e., k-word linearizable registers that support the load, store, and compare-and-swap (CAS) operations. While modern hardware supports k = 1 and sometimes k = 2 (e.g., double-width c…
K-means is a popular clustering algorithm with significant applications in numerous scientific and engineering areas. One drawback of K-means is its inability to identify non-linearly separable clusters, which may lead to inaccurate solutions in certain cases. Kernel K-means is a…
Speculative data-parallel algorithms for language recognition have been widely experimented for various types of finitestate automata (FA), deterministic (DFA) and nondeterministic (NFA), often derived fromregular expressions (RE). Such an algorithm cuts the input string into chu…
Graph Pattern Mining (GPM) has made significant progress in recent years, supporting a range of big data applications. However, GPM applications are memory-intensive as they require a tremendous amount of edge checking, which involves enumerating all possible vertex pairs and che…
Group testing is a widely used binary classification method that efficiently distinguishes between samples with and without a binary-classifiable attribute by pooling and testing subsets of a group. Bayesian Group Testing (BGT) is the state-of-the-art approach, which integrates p…
Recent GPUs have introduced Sparse Tensor Cores (SPTC) to accelerate computations on sparse matrices meeting the N:M sparse patterns. Software tools expand the support to more general V:N:M patterns. Graphs in Graph Neural Networks (GNNs) are typically sparse, but the sparsity is…
Triangle counting is a fundamental graph algorithm used to identify the number of triangles within a graph. This algorithm can be reformulated into linear algebraic operations, including sparse matrix multiplication, intersection and reduction. Modern GPUs, equipped with Tensor C…
Deep neural networks (DNNs) increasingly rely on parallel structures to enhance performance and efficiency. However, existing machine learning compilers (MLCs) face challenges in optimizing these structures due to limited parallel fusion scopes and insufficient consideration of i…
We present Reciprocating Locks, a novel mutual exclusion locking algorithm, targeting cache-coherent shared memory (CC), that enjoys a number of desirable properties. The doorway arrival phase and the Release operation both run in constant-time. Waiting threads use local spinning…
Global Storm Resolving Models (GSRMs) is crucial for understanding extreme weather events under the climate change background. In this study, we optimize Global-Regional Integrated Forecast System (GRIST), which is a unified weather-climate modeling system designed for research a…
As inference on Large Language Models (LLMs) emerges as an important workload in machine learning applications, model weight quantization has become a standard technique for efficient GPU deployment. Quantization not only reduces model size, but has also been shown to yield subst…
Relaxed semantics have been introduced to increase the achievable parallelism of concurrent data structures in exchange for weakening their ordering semantics. In this paper, we revisit the balanced allocations d-choice load balancing scheme in the context of relaxed FIFO queues.…
The Ray-Tracing (RT) core has become a widely integrated feature in modern GPUs to accelerate ray-tracing rendering. Recent research has shown that RT cores can also be repurposed to accelerate non-rendering workloads. Since the RT core essentially serves as a hardware accelerato…
While Tensor Core Units (TCUs) excel in AI tasks, their application to HPC algorithms like stencil computations faces significant challenges due to sparsity, which leads to underutilization and exacerbates memory-bound limitations. This paper introduces FlashFFTStencil1, a memory…
Software performance engineering is the science and art of making code run fast or otherwise limiting its consumption of resources, such as energy, memory footprint, network utilization, response time, etc. Performance engineering encompasses parallel computing, but it also inclu…