Spark-assisted homogeneous charge compression ignition (HCCI) combustion may be a method to improve the operation of HCCI engines. In the current study, the impact of spark assist on the fundamental properties of ignition and combustion was investigated in a single cylinder, optically-accessible research engine. Early port fuel injection and air preheating were used with indolene fuel in the st...

Recent studies concluded that the use of ammonia in SI engines is possible thanks to an ignition booster or promoter. In this paper, improvement premixed ammonia/air combustion for internal studied as a function performance and exhaust pollutants Spark-Assisted Compression Ignition single-cylinder engine, which supports higher compression ratio (CR). For first time, pure NH 3 was performed over...

The present work investigates the performance and emission characteristics of hydrous methanol fuelled Homogeneous Charge Compression Ignition (HCCI) engine. In the present work a regular diesel engine has been modified to work as HCCI engine. Hydrous methanol is used with 15% water content in this HCCI engine and its performance and emission behavior is documented. A spark plug is used for ass...

Spark is gaining wide industry adoption due to its superior performance, simple interfaces, and a rich library for analysis and calculation. Like many projects in the big data ecosystem, Spark runs on the Java Virtual Machine (JVM). Because Spark can store large amounts of data in memory, it has a major reliance on Java’s memory management and garbage collection (GC). New initiatives like Proje...

This paper analyzes the scalability potential of embarrassingly parallel genomics applications using the Apache Spark big data framework and compares their performance with native implementations as well as with Apache Hadoop scalability. The paper uses the BWA DNA mapping algorithm as an example due to its good scalability characteristics and due to the large data files it uses as input. Resul...

Nowadays, a vast amount of data is generated and collected every moment and often, this data has a spatial and/or temporal aspect. To analyze the massive data sets, big data platforms like Apache Hadoop MapReduce and Apache Spark emerged and extensions that take the spatial characteristics into account were created for them. In this paper, we analyze and compare existing solutions for spatial d...

This paper describes an investigation of the performance characteristics of high performance data analytics (HPDA) workloads on the Cray XC40TM, with a focus on commonly-used open source analytics frameworks like Apache Spark. We look at two types of Spark workloads: the Spark benchmarks from the Intel HiBench 4.0 suite and a CX matrix decomposition algorithm. We study performance from both the...

MapReduce and Spark are two very popular open source cluster computing frameworks for large scale data analytics. These frameworks hide the complexity of task parallelism and fault-tolerance, by exposing a simple programming API to users. In this paper, we evaluate the major architectural components in MapReduce and Spark frameworks including: shuffle, execution model, and caching, by using a s...

Apache Spark is a popular framework for data analytics with attractive features such as fault tolerance and interoperability with the Hadoop ecosystem. Unfortunately, many analytics operations in Spark are an order of magnitude or more slower compared to native implementations written with high performance computing tools such as MPI. There is a need to bridge the performance gap while retainin...