REtrieval-Augmented LLM-based Machine Translation (REAL-MT) shows promise for knowledge-intensive tasks like idiomatic translation, but its reliability under noisy retrieval, a common challenge in real-world deployment, remains poorly understood. To address this gap, we propose a noise synthesis framework and new metrics to systematically evaluate REAL-MT’s reliability across high-, medium-, and low-resource language pairs. Using both open- and closed-sourced models, including standard LLMs and large reasoning models (LRMs), we find that models heavily rely on retrieved context, and this dependence is significantly more detrimental in low-resource language pairs, producing nonsensical translations. Although LRMs possess enhanced reasoning capabilities, they show no improvement in error correction and are even more susceptible to noise, tending to rationalize incorrect contexts. Attention analysis reveals a shift from the source idiom to noisy content, while confidence increases despite declining accuracy, indicating poor self-monitoring. To mitigate these issues, we investigate training-free and fine-tuning strategies, which improve robustness at the cost of performance in clean contexts, revealing a fundamental trade-off. Our findings highlight the limitations of current approaches, underscoring the need for self-verifying integration mechanisms.

Depth First Search (DFS) is a fundamental graph traversal algorithm with broad applications. While existing work-stealing DFS approaches achieve strong performance on CPUs, mapping them to modern GPUs faces three major challenges: (1) limited shared memory cannot accommodate deep stacks, (2) frequent stack operations hinder efficient intra-block execution, and (3) irregular workloads complicate scalable inter-block execution.

Breadth First Search (BFS) plays a key role in computational science, networking, and artificial intelligence applications. Although the BFS approach has been extensively studied, particularly in its direction-optimized form, existing implementations still present three main issues: (1) high memory footprint; (2) the under-realized lightweight representations using bitmaps; and (3) the underuse of modern hardware such as Tensor Cores Units (TCUs).

Breadth First Search (BFS) is a fundamental algorithm in scientific computing, databases, and network analysis applications. In the algebraic BFS paradigm, each BFS iteration is expressed as a sparse matrix–vector multiplication, allowing BFS to be accelerated and analyzed through well-established linear algebra primitives. Although much effort has been made to optimize algebraic BFS on parallel platforms such as CPUs, GPUs, and distributed memory systems, vector architectures that exploit Single Instruction Multiple Data (SIMD) parallelism, particularly with their high performance on sparse workloads, remain relatively underexplored for BFS. In this paper, we propose the ALgebraic Bypass BFS Algorithm (ALBBA), a novel and efficient algebraic BFS implementation optimized for long vector architectures. ALBBA utilizes a customized variant of the SELL- C - σ data structure to fully exploit the SIMD capabilities. By integrating a vectorization-friendly search method alongside a two-level bypass strategy, we enhance both sparse matrix-sparse vector multiplication (SpMSpV) and sparse matrix-dense vector multiplication (SpMV) algorithms, which are crucial for algebraic BFS operations. We further incorporate merge primitives and adopt an efficient selection method for each BFS iteration. Our experiments on an NEC VE20B processor demonstrate that ALBBA achieves average speedups of 3.91 × , 2.88 × , and 1.46 × over Enterprise, GraphBLAST, and Gunrock running on an NVIDIA H100 GPU, respectively.

Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental building blocks in sparse linear solvers, graph processing frameworks and machine learning applications. The existing parallel approaches for shared memory SpGEMM mostly use the row-row style with possibly good parallelism. However, because of the irregularity in sparsity structures, the existing row-row methods often suffer from three problems: (1) load imbalance, (2) high global space complexity and unsatisfactory data locality, and (3) sparse accumulator selection.

With the extensive use of GPUs in modern supercomputers, accelerating sparse matrix-vector multiplication (SpMV) on GPUs received much attention in the last couple of decades. A number of techniques, such as increasing utilization of wide vector units, reducing load imbalance and selecting the best formats, have been developed. However, the 2D spatial sparsity structure has not been well exploited in the existing work for SpMV on GPUs. In this paper, we propose an efficient tiled algorithm called TileSpMV for optimizing SpMV on GPUs through exploiting 2D spatial structure of sparse matrices. We first implement seven warp-level SpMV methods for calculating sparse tiles stored in a variety of formats, and then design a selection method to find the best format and SpMV implementation for each tile. We also adaptively extract nonzeros in the very sparse tiles into a separate matrix to maximize the overall performance. The experimental results show that our method is faster than state-of-the-art SpMV methods such as Merge-SpMV, CSR5 and BSR in most matrices of the full SuiteSparse Matrix Collection and delivers up to 2.61x, 3.96x and 426.59x speedups, respectively.

The sparse triangular solve (SpTRSV) kernel is an important building block for a number of linear algebra routines such as sparse direct and iterative solvers. The major challenge of accelerating SpTRSV lies in the difficulties of finding higher parallelism. Existing work mainly focuses on reducing dependencies and synchronizations in the level-set methods. However, the 2D block layout of the input matrix has been largely ignored in designing more efficient SpTRSV algorithms.