1,184 papers · page 1 of 60
Distributed matrix-block-vector multiplication (Matvec) algorithm is a critical component of many applications, but can be computationally challenging for dense matrices of dimension O(10^6–10^7) and blocks of O(10–100) vectors. We present performance analysis, implementation, an…
With the exponential growth of computing power, large-scale scientific simulations are producing massive volumes of data, leading to critical storage and I/O challenges. Error-bounded lossy compression has become one of the most effective solutions for reducing data size while pr…
We present a new blocking linearizable stack implementation which utilizes sharding and fetch&increment to achieve significantly better performance than all existing concurrent stacks. The proposed implementation is based on a novel elimination mechanism and a new combining appro…
Sparse direct solvers are critical building blocks in a range of scientific applications on heterogeneous supercomputers. However, existing sparse direct solvers have not been able to well leverage the high bandwidth and floating-point performance of modern GPUs. The primary chal…
Zero-knowledge proofs (ZKPs) are cryptographic protocols that allow verification of statements without disclosing the underlying information. Among them, PLONK-based ZKPs are particularly notable for offering succinct, non-interactive proofs of knowledge with a universal trusted …
In high-performance applications, critical sections often become performance bottlenecks due to contention among multiple cores. Critical section delegation mitigates this overhead by consistently executing critical sections on the same core, thereby reducing contention. Traditio…
We present a new technique, Safe Concurrent Optimistic Traversals (SCOT), to address a well-known problem related to optimistic traversals with classical and more recent safe memory reclamation (SMR) schemes, such as Hazard Pointers (HP), Hazard Eras (HE), Interval-Based Reclamat…
SpMV has been widely utilized and is regarded as a significant kernel in various scientific and engineering computing applications, where its parallel performance is heavily influenced by matrix sparsity and hardware architecture. Despite extensive prior research, static partitio…
Computing attention is the backbone of transformer-based models like large language models. However, the increasing diversity of attention algorithms presents significant challenges for unleashing hardware performance. State-of-the-art variants like FlashAttention target a specif…
While Large Language Models (LLMs) are widely adopted, their massive parameter size constrains practical deployment. A common solution is clustering-based non-uniform quantization, which effectively compresses models to as low as 3 bits per weight while preserving high accuracy. …
Software transactional memory (STM) allows programmers to easily implement concurrent data structures. STMs simplify atomicity. Recent STMs can achieve good performance for some workloads but they have some limitations. In particular, STMs typically cannot support long-running re…
Large language models (LLMs) are popular around the world due to their powerful understanding capabilities. As the core component of LLMs, accelerating Transformer through parallelization has gradually become a hot research topic. Mask layers introduce sparsity into Transformer t…
We present Hapax Locks, a novel locking algorithm that is simple, enjoys constant-time arrival and unlock paths, provides FIFO admission order, and which is also space efficient and generates relatively little coherence traffic under contention in the common case. Hapax Locks off…
Recent research has focused on accelerating stencil computations by exploiting emerging hardware like Tensor Cores. To leverage these accelerators, the stencil operation must be transformed to matrix multiplications. However, this transformation introduces undesired sparsity into…
As training scales grow, collective communication libraries (CCL) increasingly face anomalies arising from complex interactions among hardware, software, and environmental factors. These anomalies typically manifest as slow/hang communication, the most frequent and time-consuming…
The growing demand for GPU resources has led to widespread shortages in data centers, prompting the exploration of CPUs as an alternative for executing GPU programs. While prior research supports executing GPU programs on single CPUs, these approaches struggle to achieve competit…
Mining temporal motifs in temporal graphs is essential for many critical applications. Although several solutions have been proposed to handle temporal motif mining, they still suffer from substantial inefficiencies due to significant redundant graph traversals and fragmented mem…
Competition for the last-level cache (LLC) is a long-standing issue in multi-tenant cloud environments, often leading to severe performance interference among co-located virtual machines. LLC management in the cloud faces unique challenges, including unpredictable tenant workload…
Automatic Differentiation (AD) is a technique that computes the derivatives of numerical programs by systematically applying the chain rule, playing a critical role in domains such as machine learning, simulation, and control systems. However, parallelizing differentiated program…
Sparse Matrix–Matrix Multiplication (SpMM) is a core kernel in scientific computing, data analytics, and artificial intelligence, supporting applications such as linear solvers and Graph Neural Networks (GNNs). The Scalable Matrix Extension (SME) in Armv9 introduces dedicated mat…