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Tongchao Jiang,1,* Haishuang Sun,2,* Tiankai Xu,1 Shuyu Xue,1 Wen Xia,2 Xiang Xiao,1 Ying Wang,1 Ling Guo,3 Huanxin Lin1 1Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People’s Republic of China; 2Department of Medical Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People’s Republic of China; 3Department of Nasopharyngeal Carcinoma, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huanxin Lin, Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of China, Email linhx@sysucc.org.cn Ling Guo, Department of Nasopharyngeal Carcinoma, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of China, Email guoling@sysucc.org.cnBackground: The C-reactive protein-albumin-lymphocyte (CALLY) score is a novel indicator associated with inflammation, immunity, and nutrition, utilized for cancer prognostic stratifica

Xin Hua,1,* Fangfang Duan,1,* Wenyu Zhai,2,* Chenge Song,1 Chang Jiang,1 Li Wang,1 Jiajia Huang,1 Huanxin Lin,3 Zhongyu Yuan1 1Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People’s Republic of China; 2Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People’s Republic of China; 3Department of Radiotherapy, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhongyu Yuan, Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People’s Republic of China, Email yuanzhy@sysucc.org.cnHuanxin Lin, Department of Radiotherapy, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People’s Republic of China, Email linhx@sysucc.org.cnPurpose: We attempted to explore the prognostic value of baseline inflammatory and nutritional biomarkers at diagnosis in patients with early-stage breast cancer and develop a novel scoring system, the inflammatory-nutritional prognostic score (INPS).Patients and Methods: We collected clinicopathological and baseline laboratory data of 1259 patients with early-stage breast cancer between December 2010 and November 2012 from Sun Yat-sen University Cancer Center. Eligible patients were randomly

General Purpose GPU computing (GPGPU) plays an increasingly vital role in high performance computing and other areas like deep learning. However, arising from the SIMD execution model, the branch divergence issue lowers efficiency of conditional branching on GPUs, and hinders the development of GPGPU. To achieve runtime on-the-spot branch divergence reduction, we propose the first on-GPU thread-data remapping scheme. Before kernel launching, our solution inserts codes into GPU kernels immediately before each target branch so as to acquire actual runtime divergence information. GPU software threads can be remapped to datasets multiple times during single kernel execution. We propose two thread-data remapping algorithms that are tailored to the GPU architecture. Effective on two generations of GPUs from both NVIDIA and AMD, our solution achieves speedups up to 2.718 with third-party benchmarks. We also implement three GPGPU frontier benchmarks from areas including computer vision, algorithmic trading and data analytics. They are hindered by more complex divergence coupled with different memory access patterns, and our solution works better than the traditional thread-data remapping scheme in all cases. As a compiler-assisted runtime solution, it can better reduce divergence for divergent applications that gain little acceleration on GPUs for the time being.

General-purpose GPUs have been prevalent for a decade. Nevertheless, GPU programming remains an onerous job practically exclusive to veteran developers who must know both domain-specific knowledge and GPU architecture well. Although current parallelizing compilers that automatically parallelize and offload sizable loops onto the GPU have helped in unfettering the power of the GPU with minimal programming effort, there are still a family of loops that carry statically non-deterministic data dependencies and cannot be parallelized. To tackle this issue, we propose two lightweight dependency checking schemes that are very different from existing conservative compilers to assist parallelizing loops with non-deterministic data dependencies. Our schemes feature linear work complexity for memory operations, lower memory consumption compared to previous work, and minimal false positives by leveraging the lockstep execution on the GPU's SIMD lanes. Experiments done using microbenchmarking and real-life applications on the latest advanced AMD discrete GPUs show that our schemes can achieve 2.2 × speedup over existing solutions in dependency-free cases while only taking about 20% of time compared to existing solutions in the case with statically unproven loop-carried dependencies.