为实现XML数据库的性能评测,提出基于TPC-C的XML数据库测试方案。针对XML数据库特性,对其数据结构、查询事务语句进行定制,将原有9张表映射成5个XML Schema文件,按照SQL/XML标准重写负载事务。应用该方案对SQL Server 2005数据库进行测...为实现XML数据库的性能评测,提出基于TPC-C的XML数据库测试方案。针对XML数据库特性,对其数据结构、查询事务语句进行定制,将原有9张表映射成5个XML Schema文件,按照SQL/XML标准重写负载事务。应用该方案对SQL Server 2005数据库进行测试,结果表明显示的各项事务特征均与TPC-C基准相同。展开更多
Itanium is the first generation product processor based on IA-64 architecture. ORC(Open Research Compil-er )provides an open source IPF(Itanium Processor Family)research compiler infrastructure. We have compiled andru...Itanium is the first generation product processor based on IA-64 architecture. ORC(Open Research Compil-er )provides an open source IPF(Itanium Processor Family)research compiler infrastructure. We have compiled andrun NAS Benchmarks on the Itanium machine. This paper briefly describes the performance of orcc, sgicc and gcc inthe following 3 ways: execution time, compilation time, and executable file size. The results show that orcc has near-ly the same performance as sgicc, which is 2 fold faster over gcc in the aspect of execution time. We also find that evenwith the best-optimized program, the utilization ratio of process resources is no more that 70%.展开更多
System-on-a-chips with intellectual property cores need a large volume of data for testing. The large volume of test data requires a large testing time and test data memory. Therefore new techniques are needed to opti...System-on-a-chips with intellectual property cores need a large volume of data for testing. The large volume of test data requires a large testing time and test data memory. Therefore new techniques are needed to optimize the test data volume, decrease the testing time, and conquer the ATE memory limitation for SOC designs. This paper presents a new compression method of testing for intellectual property core-based system-on-chip. The proposed method is based on new split- data variable length (SDV) codes that are designed using the split-options along with identification bits in a string of test data. This paper analyses the reduction of test data volume, testing time, run time, size of memory required in ATE and improvement of compression ratio. Experimental results for ISCAS 85 and ISCAS 89 Benchmark circuits show that SDV codes outperform other compression methods with the best compression ratio for test data compression. The decompression architecture for SDV codes is also presented for decoding the implementations of compressed bits. The proposed scheme shows that SDV codes are accessible to any of the variations in the input test data stream.展开更多
Background:Large language models(LLMs)have shown promise in educational applications,but their performance on high-stakes admissions tests,such as the Dental Admission Test(DAT),remains unclear.Understanding the capab...Background:Large language models(LLMs)have shown promise in educational applications,but their performance on high-stakes admissions tests,such as the Dental Admission Test(DAT),remains unclear.Understanding the capabilities and limitations of these models is critical for determining their suitability in test preparation.Methods:This study evaluated the ability of 16 LLMs,including general-purpose models(e.g.,GPT-3.5,GPT-4,GPT-4o,GPT-o1,Google’s Bard,mistral-large,and Claude),domain-specific finetuned models(e.g.,DentalGPT,MedGPT,and BioGPT),and open-source models(e.g.,Llama2-7B,Llama2-13B,Llama2-70B,Llama3-8B,and Llama3-70B),to answer questions from a sample DAT.Quantitative analysis was performed to assess model accuracy in different sections,and qualitative thematic analysis by subject matter experts examined specific challenges encountered by the models.Results:GPT-4o and GPT-o1 outperformed others in text-based questions assessing knowledge and comprehension,with GPT-o1 achieving perfect scores in the natural sciences(NS)and reading comprehension(RC)sections.Open-source models such as Llama3-70B also performed competitively in RC tasks.However,all models,including GPT-4o,struggled substantially with perceptual ability(PA)items,highlighting a persistent limitation in handling image-based tasks requiring visual-spatial reasoning.Fine-tuned medical models(e.g.,DentalGPT,MedGPT,and BioGPT)demonstrated moderate success in text-based tasks but underperformed in areas requiring critical thinking and reasoning.Thematic analysis identified key challenges,including difficulties with stepwise problem-solving,transferring knowledge,comprehending intricate questions,and hallucinations,particularly on advanced items.Conclusions:While LLMs show potential for reinforcing factual knowledge and supporting learners,their limitations in handling higherorder cognitive tasks and image-based reasoning underscore the need for judicious integration with instructor-led guidance and targeted practice.This study provides valuable insights into the capabilities and limitations of current LLMs in preparing prospective dental students and highlights pathways for future innovations to improve performance across all cognitive skills assessed by the DAT.展开更多
文摘为实现XML数据库的性能评测,提出基于TPC-C的XML数据库测试方案。针对XML数据库特性,对其数据结构、查询事务语句进行定制,将原有9张表映射成5个XML Schema文件,按照SQL/XML标准重写负载事务。应用该方案对SQL Server 2005数据库进行测试,结果表明显示的各项事务特征均与TPC-C基准相同。
文摘Itanium is the first generation product processor based on IA-64 architecture. ORC(Open Research Compil-er )provides an open source IPF(Itanium Processor Family)research compiler infrastructure. We have compiled andrun NAS Benchmarks on the Itanium machine. This paper briefly describes the performance of orcc, sgicc and gcc inthe following 3 ways: execution time, compilation time, and executable file size. The results show that orcc has near-ly the same performance as sgicc, which is 2 fold faster over gcc in the aspect of execution time. We also find that evenwith the best-optimized program, the utilization ratio of process resources is no more that 70%.
文摘System-on-a-chips with intellectual property cores need a large volume of data for testing. The large volume of test data requires a large testing time and test data memory. Therefore new techniques are needed to optimize the test data volume, decrease the testing time, and conquer the ATE memory limitation for SOC designs. This paper presents a new compression method of testing for intellectual property core-based system-on-chip. The proposed method is based on new split- data variable length (SDV) codes that are designed using the split-options along with identification bits in a string of test data. This paper analyses the reduction of test data volume, testing time, run time, size of memory required in ATE and improvement of compression ratio. Experimental results for ISCAS 85 and ISCAS 89 Benchmark circuits show that SDV codes outperform other compression methods with the best compression ratio for test data compression. The decompression architecture for SDV codes is also presented for decoding the implementations of compressed bits. The proposed scheme shows that SDV codes are accessible to any of the variations in the input test data stream.
基金partially supported by the National Institutes of Health’s National Center for Complementary and Integrative Health under grant number R01AT009457National Institute on Aging under grant number R01AG078154National Cancer Institute under grant number R01CA287413.
文摘Background:Large language models(LLMs)have shown promise in educational applications,but their performance on high-stakes admissions tests,such as the Dental Admission Test(DAT),remains unclear.Understanding the capabilities and limitations of these models is critical for determining their suitability in test preparation.Methods:This study evaluated the ability of 16 LLMs,including general-purpose models(e.g.,GPT-3.5,GPT-4,GPT-4o,GPT-o1,Google’s Bard,mistral-large,and Claude),domain-specific finetuned models(e.g.,DentalGPT,MedGPT,and BioGPT),and open-source models(e.g.,Llama2-7B,Llama2-13B,Llama2-70B,Llama3-8B,and Llama3-70B),to answer questions from a sample DAT.Quantitative analysis was performed to assess model accuracy in different sections,and qualitative thematic analysis by subject matter experts examined specific challenges encountered by the models.Results:GPT-4o and GPT-o1 outperformed others in text-based questions assessing knowledge and comprehension,with GPT-o1 achieving perfect scores in the natural sciences(NS)and reading comprehension(RC)sections.Open-source models such as Llama3-70B also performed competitively in RC tasks.However,all models,including GPT-4o,struggled substantially with perceptual ability(PA)items,highlighting a persistent limitation in handling image-based tasks requiring visual-spatial reasoning.Fine-tuned medical models(e.g.,DentalGPT,MedGPT,and BioGPT)demonstrated moderate success in text-based tasks but underperformed in areas requiring critical thinking and reasoning.Thematic analysis identified key challenges,including difficulties with stepwise problem-solving,transferring knowledge,comprehending intricate questions,and hallucinations,particularly on advanced items.Conclusions:While LLMs show potential for reinforcing factual knowledge and supporting learners,their limitations in handling higherorder cognitive tasks and image-based reasoning underscore the need for judicious integration with instructor-led guidance and targeted practice.This study provides valuable insights into the capabilities and limitations of current LLMs in preparing prospective dental students and highlights pathways for future innovations to improve performance across all cognitive skills assessed by the DAT.
