Build Kernels For Task-based DNN Inference Runtime

Distributed deep learning systems face several challenges in eciently scaling inference tasks due to communication overhead, load imbalance, and underutilized computational resources. These challenges are particularly common in large language models that incorporate Mixture-of-Experts layers and autoregressive attention layers. To address these challenges, we propose an asynchronous task-based distributed inference runtime that builds on the FlexFlow framework and optimizes kernels for Mixture-of-Experts (MoE) and Incremental MultiHead Self-Attention (IncMHA) layers in inference tasks. We introduce the optimized FlexFlow Experts Operator and FlexFlow IncMHA Operator, which leverage the asynchronous nature of inference tasks to achieve better GPU utilization and lower communication latency. It allows Flexflow to accommodate data-independent requests and read-only weights while being resilient to varying arrival rates and accommodating optimal batch configurations. We evaluated our system against existing frameworks and demonstrated its e↵ectiveness in improving performance and resource utilization. Our future work will focus on enabling better parallelism in large models by decoupling autoregressive operations with speculative inference. </p

Additional file 2 of Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Additional file 2: Supplementary table 1. Univariate and Multivariate analysis of prognostic factors affecting OS

Additional file 3 of Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Additional file 3: Supplementary table 2. Univariate and Multivariate analysis of prognostic factors affecting CS

Additional file 5 of Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Additional file 5: Supplementary table 3. Discriminatory ability of clinicopathological factors in predicting OS in gastric cancer

Additional file 8 of Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Additional file 8

Additional file 1 of Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Additional file 1. Material and Methods

Additional file 7 of Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Additional file 7: Supplementary figure 3. Survival analysis of CSS and OS stratified by tumor size

Additional file 4 of Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Additional file 4: Supplementary figure 2. Heatmap of C-index of clinicopathological factors in predicting CSS and OS in gastric cancer

Additional file 6 of Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis

Additional file 6: Supplementary table 4. C-index of clinicopathological factors in subgroup

Image_1_Metabolomics analysis of stool in rats with type 2 diabetes mellitus after single-anastomosis duodenal–ileal bypass with sleeve gastrectomy.tif

BackgroundSingle-anastomosis duodenal-ileal bypass with sleeve gastrectomy (SADI-S) is one of the most effective bariatric procedures in the treatment of type 2 diabetes mellitus (T2DM). However, the mechanisms by which SADI-S improves T2DM are not well-known.ObjectiveTo explore the effects of SADI-S on metabolites in the stool of rats with T2DM.MethodsTwenty rats were fed on high-fat diet and administered with a low-dose (30mg/kg) of streptozotocin to establish T2DM models. The rats were then randomly assigned to the SADI-S group (n=10) and sham operation group (n=9). Stool samples were collected from all rats at 8 weeks after surgery and stored at -80 °C. Metabolomics analysis was performed to identify differential metabolites through ultra- performance liquid chromatography-mass spectrometry.ResultsAt 8-week after surgery, rats of the SADI-S group showed significantly decreased fasting blood glucose, glucose tolerance test 2-hour, glycated haemoglobin, and body weight compared with those of the sham group. A total of 245 differential metabolites were identified between the two groups. Among them, 16 metabolites such as branched-chain amino acids (valine), aromatic amino acid (phenylalanine), bile acid (cholic acid, lithocholic acid, and β-muricholic acid), short-chain fatty acid (isobutyric acid), and phospholipid [lysoPE(17:0), lysoPE(20:3) and lysoPS(16:0)] were associated to the T2DM remission after SADI-S.ConclusionSADI-S improves T2DM in rats by regulating phenylalanine biosynthesis, valine, phenylalanine, alanine, glutamate, proline, bile acid, and phospholipid metabolism pathways.</p