MOTIVATION: Genome-wide association studies (GWAS) at biobank scale are computationally intensive, especially for admixed populations requiring robust statistical models. SAIGE is a widely used method for generalized linear mixed-model GWAS but is limited by its CPU-based implementation, making phenome-wide association studies impractical for many research groups. RESULTS: We developed SAIGE-GPU, a GPU-accelerated version of SAIGE that replaces CPU-intensive matrix operations with GPU-optimized kernels. The core innovation is distributing genetic relationship matrix calculations across GPUs and communication layers. Applied to 2068 phenotypes from 635 969 participants in the Million Veteran Program, including diverse and admixed populations, SAIGE-GPU achieved a 5-fold speedup in mixed model fitting on supercomputing infrastructure and cloud platforms. We further optimized the variant association testing step through multi-core and multi-trait parallelization. Deployed on Google Cloud Platform and Azure, the method provided substantial cost and time savings. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are available for download at https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3. A code snapshot is archived at Zenodo for reproducibility (DOI: [10.5281/zenodo.17642591]). SAIGE-GPU is available in a containerized format for use across HPC and cloud environments and is implemented in R/C++ and runs on Linux systems.

MRAM is a one of the resistive memory which is based on a change in resistance of a unit cell. For successful read operation, resistive memory requires a reference resistance which has middle resistance values of data 0 and 1. Generally, the read method used in MRAM is to use a reference cell having the intermediate resistance. This conventional method has weakness on read disturb rate and temperature change. To avoid these weaknesses, new read scheme has been developed that sets reference resistances with controllable resistances instead of reference cells. But developed method has difficulty to set the stable reference resistance value. This paper presents the algorithmic trim method of finding the reference resistance by adjusting the controllable resistor. The number of failed bits for each read resistance was examined to find stable reference resistance conditions. In addition, a sampling method and a binary search method were introduced to improve test efficiency. The problem that occurs when applying binary search is solved by exception processing. And for parallel testing for each chip, the method that modifying the vector memory of the tester was used. Using algorithmic read resistance trim, yield was improved by 23.7%and test time was reduced by 80%. Also, test time of trim module was reduced by 95% compared to the beginning (<0.4 second). This trim method has been widespread in the MRAM product of Samsung Foundry, which adjusts the reference resistance with a controllable resistor.

SVM (Support Vector Machine) is a well-established machine learning methodology popularly used for classification, regression, and ranking. Recently SVM has been actively researched for rank learning and applied to various applications including search engines or relevance feedback systems. A query in such systems is the ranking function F learned by SVM. Once learning a function F or formulating the query, processing the query to find top-k results requires evaluating the entire database by F.So far, there exists no exact indexing solution for SVM functions. Existing top-k query processing algorithms are not applicable to the machine-learned ranking functions, as they often make restrictive assumptions on the query, such as linearity or monotonicity of functions. Existing metric-based or reference-based indexing methods are also not applicable, because data points are invisible in the kernel space (SVM feature space) on which the index must be built. Existing kernel indexing methods return approximate results or fix kernel parameters. This paper proposes an exact indexing solution for SVM functions with varying kernel parameters.We first propose key geometric properties of the kernel space -- ranking instability and ordering stability -- which is crucial for building indices in the kernel space. Based on them, we develop an index structure iKernel and processing algorithms. We then present clustering techniques in the kernel space to enhance the pruning effectiveness of the index. According to our experiments, iKernel is highly effective overall producing 1~5% of evaluation ratio on large data sets. According to our best knowledge, iKernel is the first indexing solution that finds exact top-k results of SVM functions without a full scan of data set.

As more and more data become accessible, ranking query semantics such as ranked retrieval, possibly combined with Boolean query conditions, has gained a lot of attention lately. As the formulation of such queries is known to be difficult, we aim at providing quick approximate answers as cues for interactive query refinements. Toward the goal, we study approximate answering techniques for Boolean+ranking queries. While approximate query answering has been studied for Boolean-only queries, we observed that a straightforward extension of this work for advanced queries incurs prohibitive overheads. We thus propose a systematic framework which significantly outperforms such a naive extension. We also empirically validate the effectiveness and efficiency of our framework.