On the classification and evaluation of prefetching schemes

Abstract available: p. [2

Prefetching techniques for client server object-oriented database systems

The performance of many object-oriented database applications suffers from the page fetch latency which is determined by the expense of disk access. In this work we suggest several prefetching techniques to avoid, or at least to reduce, page fetch latency. In practice no prediction technique is perfect and no prefetching technique can entirely eliminate delay due to page fetch latency. Therefore we are interested in the trade-off between the level of accuracy required for obtaining good results in terms of elapsed time reduction and the processing overhead needed to achieve this level of accuracy. If prefetching accuracy is high then the total elapsed time of an application can be reduced significantly otherwise if the prefetching accuracy is low, many incorrect pages are prefetched and the extra load on the client, network, server and disks decreases the whole system performance. Access pattern of object-oriented databases are often complex and usually hard to predict accurately. The ..

Computer-language based data prefetching techniques

Data prefetching has long been used as a technique to improve access times to persistent data. It is based on retrieving data records from persistent storage to main memory before the records are needed. Data prefetching has been applied to a wide variety of persistent storage systems, from file systems to Relational Database Management Systems and NoSQL databases, with the aim of reducing access times to the data maintained by the system and thus improve the execution times of the applications using this data. However, most existing solutions to data prefetching have been based on information that can be retrieved from the storage system itself, whether in the form of heuristics based on the data schema or data access patterns detected by monitoring access to the system. There are multiple disadvantages of these approaches in terms of the rigidity of the heuristics they use, the accuracy of the predictions they make and / or the time they need to make these predictions, a process often performed while the applications are accessing the data and causing considerable overhead. In light of the above, this thesis proposes two novel approaches to data prefetching based on predictions made by analyzing the instructions and statements of the computer languages used to access persistent data. The proposed approaches take into consideration how the data is accessed by the higher-level applications, make accurate predictions and are performed without causing any additional overhead. The first of the proposed approaches aims at analyzing instructions of applications written in object-oriented languages in order to prefetch data from Persistent Object Stores. The approach is based on static code analysis that is done prior to the application execution and hence does not add any overhead. It also includes various strategies to deal with cases that require runtime information unavailable prior to the execution of the application. We integrate this analysis approach into an existing Persistent Object Store and run a series of extensive experiments to measure the improvement obtained by prefetching the objects predicted by the approach. The second approach analyzes statements and historic logs of the declarative query language SPARQL in order to prefetch data from RDF Triplestores. The approach measures two types of similarity between SPARQL queries in order to detect recurring query patterns in the historic logs. Afterwards, it uses the detected patterns to predict subsequent queries and launch them before they are requested to prefetch the data needed by them. Our evaluation of the proposed approach shows that it high-accuracy prediction and can achieve a high cache hit rate when caching the results of the predicted queries.Precargar datos ha sido una de las técnicas más comunes para mejorar los tiempos de acceso a datos persistentes. Esta técnica se basa en predecir los registros de datos que se van a acceder en el futuro y cargarlos del almacenimiento persistente a la memoria con antelación a su uso. Precargar datos ha sido aplicado en multitud de sistemas de almacenimiento persistente, desde sistemas de ficheros a bases de datos relacionales y NoSQL, con el objetivo de reducir los tiempos de acceso a los datos y por lo tanto mejorar los tiempos de ejecución de las aplicaciones que usan estos datos. Sin embargo, la mayoría de los enfoques existentes utilizan predicciones basadas en información que se encuentra dentro del mismo sistema de almacenimiento, ya sea en forma de heurísticas basadas en el esquema de los datos o patrones de acceso a los datos generados mediante la monitorización del acceso al sistema. Estos enfoques presentan varias desventajas en cuanto a la rigidez de las heurísticas usadas, la precisión de las predicciones generadas y el tiempo que necesitan para generar estas predicciones, un proceso que se realiza con frecuencia mientras las aplicaciones acceden a los datos y que puede tener efectos negativos en el tiempo de ejecución de estas aplicaciones. En vista de lo anterior, esta tesis presenta dos enfoques novedosos para precargar datos basados en predicciones generadas por el análisis de las instrucciones y sentencias del lenguaje informático usado para acceder a los datos persistentes. Los enfoques propuestos toman en consideración cómo las aplicaciones acceden a los datos, generan predicciones precisas y mejoran el rendimiento de las aplicaciones sin causar ningún efecto negativo. El primer enfoque analiza las instrucciones de applicaciones escritas en lenguajes de programación orientados a objetos con el fin de precargar datos de almacenes de objetos persistentes. El enfoque emplea análisis estático de código hecho antes de la ejecución de las aplicaciones, y por lo tanto no afecta negativamente el rendimiento de las mismas. El enfoque también incluye varias estrategias para tratar casos que requieren información de runtime no disponible antes de ejecutar las aplicaciones. Además, integramos este enfoque en un almacén de objetos persistentes y ejecutamos una serie extensa de experimentos para medir la mejora de rendimiento que se puede obtener utilizando el enfoque. Por otro lado, el segundo enfoque analiza las sentencias y logs del lenguaje declarativo de consultas SPARQL para precargar datos de triplestores de RDF. Este enfoque aplica dos medidas para calcular la similtud entre las consultas del lenguaje SPARQL con el objetivo de detectar patrones recurrentes en los logs históricos. Posteriormente, el enfoque utiliza los patrones detectados para predecir las consultas siguientes y precargar con antelación los datos que necesitan. Nuestra evaluación muestra que este enfoque produce predicciones de alta precisión y puede lograr un alto índice de aciertos cuando los resultados de las consultas predichas se guardan en el caché.Postprint (published version

Computer-language based data prefetching techniques

Data prefetching has long been used as a technique to improve access times to persistent data. It is based on retrieving data records from persistent storage to main memory before the records are needed. Data prefetching has been applied to a wide variety of persistent storage systems, from file systems to Relational Database Management Systems and NoSQL databases, with the aim of reducing access times to the data maintained by the system and thus improve the execution times of the applications using this data. However, most existing solutions to data prefetching have been based on information that can be retrieved from the storage system itself, whether in the form of heuristics based on the data schema or data access patterns detected by monitoring access to the system. There are multiple disadvantages of these approaches in terms of the rigidity of the heuristics they use, the accuracy of the predictions they make and / or the time they need to make these predictions, a process often performed while the applications are accessing the data and causing considerable overhead. In light of the above, this thesis proposes two novel approaches to data prefetching based on predictions made by analyzing the instructions and statements of the computer languages used to access persistent data. The proposed approaches take into consideration how the data is accessed by the higher-level applications, make accurate predictions and are performed without causing any additional overhead. The first of the proposed approaches aims at analyzing instructions of applications written in object-oriented languages in order to prefetch data from Persistent Object Stores. The approach is based on static code analysis that is done prior to the application execution and hence does not add any overhead. It also includes various strategies to deal with cases that require runtime information unavailable prior to the execution of the application. We integrate this analysis approach into an existing Persistent Object Store and run a series of extensive experiments to measure the improvement obtained by prefetching the objects predicted by the approach. The second approach analyzes statements and historic logs of the declarative query language SPARQL in order to prefetch data from RDF Triplestores. The approach measures two types of similarity between SPARQL queries in order to detect recurring query patterns in the historic logs. Afterwards, it uses the detected patterns to predict subsequent queries and launch them before they are requested to prefetch the data needed by them. Our evaluation of the proposed approach shows that it high-accuracy prediction and can achieve a high cache hit rate when caching the results of the predicted queries.Precargar datos ha sido una de las técnicas más comunes para mejorar los tiempos de acceso a datos persistentes. Esta técnica se basa en predecir los registros de datos que se van a acceder en el futuro y cargarlos del almacenimiento persistente a la memoria con antelación a su uso. Precargar datos ha sido aplicado en multitud de sistemas de almacenimiento persistente, desde sistemas de ficheros a bases de datos relacionales y NoSQL, con el objetivo de reducir los tiempos de acceso a los datos y por lo tanto mejorar los tiempos de ejecución de las aplicaciones que usan estos datos. Sin embargo, la mayoría de los enfoques existentes utilizan predicciones basadas en información que se encuentra dentro del mismo sistema de almacenimiento, ya sea en forma de heurísticas basadas en el esquema de los datos o patrones de acceso a los datos generados mediante la monitorización del acceso al sistema. Estos enfoques presentan varias desventajas en cuanto a la rigidez de las heurísticas usadas, la precisión de las predicciones generadas y el tiempo que necesitan para generar estas predicciones, un proceso que se realiza con frecuencia mientras las aplicaciones acceden a los datos y que puede tener efectos negativos en el tiempo de ejecución de estas aplicaciones. En vista de lo anterior, esta tesis presenta dos enfoques novedosos para precargar datos basados en predicciones generadas por el análisis de las instrucciones y sentencias del lenguaje informático usado para acceder a los datos persistentes. Los enfoques propuestos toman en consideración cómo las aplicaciones acceden a los datos, generan predicciones precisas y mejoran el rendimiento de las aplicaciones sin causar ningún efecto negativo. El primer enfoque analiza las instrucciones de applicaciones escritas en lenguajes de programación orientados a objetos con el fin de precargar datos de almacenes de objetos persistentes. El enfoque emplea análisis estático de código hecho antes de la ejecución de las aplicaciones, y por lo tanto no afecta negativamente el rendimiento de las mismas. El enfoque también incluye varias estrategias para tratar casos que requieren información de runtime no disponible antes de ejecutar las aplicaciones. Además, integramos este enfoque en un almacén de objetos persistentes y ejecutamos una serie extensa de experimentos para medir la mejora de rendimiento que se puede obtener utilizando el enfoque. Por otro lado, el segundo enfoque analiza las sentencias y logs del lenguaje declarativo de consultas SPARQL para precargar datos de triplestores de RDF. Este enfoque aplica dos medidas para calcular la similtud entre las consultas del lenguaje SPARQL con el objetivo de detectar patrones recurrentes en los logs históricos. Posteriormente, el enfoque utiliza los patrones detectados para predecir las consultas siguientes y precargar con antelación los datos que necesitan. Nuestra evaluación muestra que este enfoque produce predicciones de alta precisión y puede lograr un alto índice de aciertos cuando los resultados de las consultas predichas se guardan en el caché

Improving Instruction Fetch Rate with Code Pattern Cache for Superscalar Architecture

In the past, instruction fetch speeds have been improved by using cache schemes that capture the actual program flow. In this proposal, we present the architecture of a new instruction cache named code pattern cache (CPC); the cache is used with superscalar processors. CPC?s operation is based on the fundamental principles that: common programs tend to repeat their execution patterns; and efficient storage of a program flow can enhance the performance of an instruction fetch mechanism. CPC saves basic blocks (sets of instructions separated by control instructions) and their boundary addresses while the code is running. Basic blocks and their addresses are stored in two separate structures, called block pointer cache (BPC) and basic block cache (BBC), respectively. Later, if the same basic block sequence is expected to execute, it is fetched from CPC, instead of the instruction cache; this mechanism results in higher likelihood of delivering a larger number of instructions in every clock cycle. We developed single and multi-threaded simulators for TC, BC, and CPC, and used them with 10 SPECint2000 benchmarks. The simulation results demonstrated CPC?s advantage over TC and BC, in terms of trace miss rate and average trace length. Additionally, we used cache models to quantify the timing, area, and power for the three cache schemes. Using an aggregate performance index that combined the simulation and modeling results, CPC was shown to perform better than both TC and BC. During our research, each of the TC-, BC-, or CPC- configurations took 4-6 hours to simulate, so performance comparison of these caches proved to be a very time-consuming process. Neural network models (NNM?s) can be time-efficient alternatives to simulations, so we studied their feasibility to represent the cache behavior. We developed two NNM\u27s, one to predict the trace miss rate and the other to predict the average trace length for the three caches. The NNM\u27s modeled the caches with reasonable accuracy, and produced results in a fraction of a second

Impact Of Thread Scheduling On Modern Gpus

The Graphics Processing Unit (GPU) has become a more important component in high-performance computing systems as it accelerates data and compute intensive applications significantly with less cost and power. The GPU achieves high performance by executing massive number of threads in parallel in a SPMD (Single Program Multiple Data) fashion. Threads are grouped into workgroups by programmer and workgroups are then assigned to each compute core on the GPU by hardware. Once assigned, a workgroup is further subgrouped into wavefronts of the fixed number of threads by hardware when executed in a SIMD (Single Instruction Multiple Data) fashion. In this thesis, we investigated the impact of thread (at workgroup and wavefront level) scheduling on overall hardware utilization and performance. We implement four different thread schedulers: Two-level wavefront scheduler, Lookahead wavefront scheduler and Two-level + Lookahead wavefront scheduler, and Block workgroup scheduler. We implement and test these schedulers on a cycle accurate detailed architectural simulator called Multi2Sim targeting AMD\u27s latest Graphics Core Next (GCN) architecture. Our extensive evaluation and analysis show that using some of these alternate mechanisms, cache hit rate is improved by an average of 30% compared to the baseline round-robin scheduler, thus drastically reducing the number of stalls caused by long latency memory operations. We also observe that some of these schedulers improve overall performance by an average of 17% compared to the baseline

Instruction prefetch strategies in a pipelined processor

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1983.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERINGIncludes bibliographical references.by Hubert Rae McLellan, Jr.M.S

Methods and systems for ordering instructions using future values

A method of ordering instructions. The method can include placing a first instruction that consumes a value of an object before a second instruction that produces the value of the object such that the first instruction is processed before the second instruction and a physical location is allocated to the value of the object upon processing the first instruction.https://digitalcommons.mtu.edu/patents/1022/thumbnail.jp