MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions

Deep neural networks are widely used in personalized recommendation systems. Unlike regular DNN inference workloads, recommendation inference is memory-bound due to the many random memory accesses needed to lookup the embedding tables. The inference is also heavily constrained in terms of latency because producing a recommendation for a user must be done in about tens of milliseconds. In this paper, we propose MicroRec, a high-performance inference engine for recommendation systems. MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups. We have implemented the resulting design on an FPGA board including the embedding lookup step as well as the complete inference process. Compared to the optimized CPU baseline (16 vCPU, AVX2-enabled), MicroRec achieves 13.8~14.7x speedup on embedding lookup alone and 2.5$~5.4x speedup for the entire recommendation inference in terms of throughput. As for latency, CPU-based engines needs milliseconds for inferring a recommendation while MicroRec only takes microseconds, a significant advantage in real-time recommendation systems.Comment: Accepted by MLSys'21 (the 4th Conference on Machine Learning and Systems

Vegetation response to changes in climate across different climate zones in China

Vegetation growth is sensitive to climate change. The complex climate types of China pose great challenges to the sustainable management of vegetation on global change. Therefore, this study used Enhanced Vegetation Index (EVI) as an indicator to explore the spatiotemporal dynamics of vegetation and their driving factors in different climatic zones of China to provide theoretical support for sustainable vegetation management in different climate zones in the future. The results showed that vegetation exhibited considerable clustering patterns in the country, with high and low values concentrated in the eastern and western regions, respectively. From 2001 to 2020, both at regional and pixel scales, vegetation in China showed a significant greening trend. EVI displayed a noticeable increase within temperate and subtropical areas. The only exception is observed in the eastern coastal area of the North China Plain and Yangtze River Delta region, which experienced evident degradation trend. During this period, China's climate showed an overall trend towards warming and humidification with drying trends observed mainly over the western regions. The impact of climate changes resulted in EVI dynamics that vary over time and space. The vegetation change in China was mainly derived by changes in precipitation and radiation rather than temperature, especially in temperate and subfrigid regions. Precipitation was the main driving factor for vegetation greening in tropical and temperate regions, while radiation and temperature were the dominant climate factor for vegetation greening in subfrigid and subtropical regions, respectively. When precipitation was no longer a limiting factor for vegetation growth, the effect of temperature or radiation increases. In addition, the positive impact of precipitation on plant growth in temperate regions was much greater than that of radiation and temperature, and this difference was much greater than in tropical, subtropical, and subfrigid regions

Genomic Island-Encoded Diguanylate Cyclase from <i>Vibrio alginolyticus</i> Regulates Biofilm Formation and Motility in <i>Pseudoalteromonas</i>

Many bacteria use the second messenger c-di-GMP to regulate exopolysaccharide production, biofilm formation, motility, virulence, and other phenotypes. The c-di-GMP level is controlled by the complex network of diguanylate cyclases (DGCs) and phosphodiesterases (PDEs) that synthesize and degrade c-di-GMP. In addition to chromosomally encoded DGCs, increasing numbers of DGCs were found to be located on mobile genetic elements. Whether these mobile genetic element-encoded DGCs can modulate the physiological phenotypes in recipient bacteria after horizontal gene transfer should be investigated. In our previous study, a genomic island encoding three DGC proteins (Dgc137, Dgc139, and Dgc140) was characterized in Vibrio alginolyticus isolated from the gastric cavity of the coral Galaxea fascicularis. Here, the effect of the three DGCs in four Pseudoalteromonas strains isolated from coral Galaxea fascicularis and other marine environments was explored. The results showed that when dgc137 is present rather than the three DGC genes, it obviously modulates biofilm formation and bacterial motility in these Pseudoalteromonas strains. Our findings implied that mobile genetic element-encoded DGC could regulate the physiological status of neighboring bacteria in a microbial community by modulating the c-di-GMP level after horizontal gene transfer

Dual blockade of EGFR and PI3K signaling pathways offers a therapeutic strategy for glioblastoma

Abstract Background Glioblastoma multiforme (GBM) is a devastating disease that lacks effective drugs for targeted therapy. Previously, we found that the third-generation epidermal growth factor receptor (EGFR) inhibitor AZD-9291 persistently blocked the activation of the ERK pathway but had no inhibitory effect on the phosphoinositide 3-kinase (PI3K)/Akt pathway. Given that the PI3K inhibitor GDC-0084 is being evaluated in phase I/II clinical trials of GBM treatment, we hypothesized that combined inhibition of the EGFR/ERK and PI3K/Akt pathways may have a synergistic effect in the treatment of GBM. Methods The synergistic effects of cotreatment with AZD-9291 and GDC-0084 were validated using cell viability assays in GBM and primary GBM cell lines. Moreover, the underlying inhibitory mechanisms were assessed through colony formation, EdU proliferation, and cell cycle assays, as well as RNA-seq analyses and western blot. The therapeutic effects of the drug combination on tumor growth and survival were investigated in mice bearing tumors using subcutaneously or intracranially injected LN229 xenografts. Results Combined treatment with AZD-9291 and GDC-0084 synergistically inhibited the proliferation and clonogenic survival, as well as induced cell cycle arrest of GBM cells and primary GBM cells, compared to monotherapy. Moreover, AZD-9291 plus GDC-0084 combination therapy significantly inhibited the growth of subcutaneous tumors and orthotopic brain tumor xenografts, thus prolonging the survival of tumor-bearing mice. More importantly, the combination of AZD-9291 and GDC-0084 simultaneously blocked the activation of the EGFR/MEK/ERK and PI3K/AKT/mTOR signaling pathways, thereby exerting significant antitumor activity. Conclusion Our findings demonstrate that the combined blockade of the EGFR/MEK/ERK and PI3K/AKT/mTOR pathways is more effective against GBM than inhibition of each pathway alone, both in vitro and in vivo. Our results suggest that AZD-9291 combined with GDC-0084 may be considered as a potential treatment strategy in future clinical trials. Video Abstrac

Biomarkers and experimental models for cancer immunology investigation

Abstract The rapid advancement of tumor immunotherapies poses challenges for the tools used in cancer immunology research, highlighting the need for highly effective biomarkers and reproducible experimental models. Current immunotherapy biomarkers encompass surface protein markers such as PD‐L1, genetic features such as microsatellite instability, tumor‐infiltrating lymphocytes, and biomarkers in liquid biopsy such as circulating tumor DNAs. Experimental models, ranging from 3D in vitro cultures (spheroids, submerged models, air–liquid interface models, organ‐on‐a‐chips) to advanced 3D bioprinting techniques, have emerged as valuable platforms for cancer immunology investigations and immunotherapy biomarker research. By preserving native immune components or coculturing with exogenous immune cells, these models replicate the tumor microenvironment in vitro. Animal models like syngeneic models, genetically engineered models, and patient‐derived xenografts provide opportunities to study in vivo tumor‐immune interactions. Humanized animal models further enable the simulation of the human‐specific tumor microenvironment. Here, we provide a comprehensive overview of the advantages, limitations, and prospects of different biomarkers and experimental models, specifically focusing on the role of biomarkers in predicting immunotherapy outcomes and the ability of experimental models to replicate the tumor microenvironment. By integrating cutting‐edge biomarkers and experimental models, this review serves as a valuable resource for accessing the forefront of cancer immunology investigation

FleetRec: Large-Scale Recommendation Inference on Hybrid GPU-FPGA Clusters

We present FleetRec, a high-performance and scalable recommendation inference system within tight latency constraints. FleetRec takes advantage of heterogeneous hardware including GPUs and the latest FPGAs equipped with high-bandwidth memory. By disaggregating computation and memory to different types of hardware and bridging their connections by high-speed network, FleetRec gains the best of both worlds, and can naturally scale out by adding nodes to the cluster. Experiments on three production models up to 114 GB show that FleetRec outperforms optimized CPU baseline by more than one order of magnitude in terms of throughput while achieving significantly lower latency