A Micro-Benchmark Suite for Evaluating HDFS Operations on Modern Clusters HDFS Performace Factors • Performance of HDFS is determined by the following 3 factors • Factors related to storage and network configurations • Controllable parameters (block size, packet size) • Data Access Pattern • A benchmark tool suite to evaluate HDFS performance metrics in different configurations is addressed in this paper. HDFS Benchmarks • Most popular benchmark to evaluate HDFS I/O performance is TestDFSIO • But, it involves the MapReduce framework. • There is a lack of standardized benchmark suite to evaluate the performance of just standalone HDFS. Designing such a HDFS benchmark suite • HDFS has three main operations • Sequential Write. • Sequential Read. • Random Read. • HDFS performance is measured by the latency and throughput of these operations • Performance is influenced by underlying network, storage, HDFS configuration parameters and data access patterns • This benchmark suite • Focuses on three kinds of data access patterns: sequential, random and mix (read and write) • Has options to set HDFS configuration parameters dynamically Benchmark Suite • Five different benchmarks • • • • • Sequential Write Latency (SWL) Sequential or Random Read Latency (SRL or RRL) Sequential Write Throughput (SWT) Sequential Read Throughput (SRT) Sequential Read - Write Throughput (SRWT) • All the above benchmarks use HDFS API for Read and Write Benchmark Parameter List The benchmark suite calculates statistics like min, max and average of latency and throughput. Experimental Setup • Intel Westmere Cluster • Each node has 8 processor cores on 2 Intel Xeon 2.67 GHz Quadcore CPUs, 12 GB main memory, 160 GB hard disk • Network: 1GigE and IPoIB (32Gbps) Sequential Write Latency (SWL) Sequential and Random Read Latency (SRL / RRL) Sequential Write Throughput (SWT with 4 writers) Sequential Read Throughput (SRT) Evaluation of Mixed Overload (SRWT with 4 readers , 4 writers) Conclusion • Design, development and implementation of a micro benchmark suite to evaluate performance of standalone HDFS • Flexible infrastructure for the benchmarks to set HDFS configuration parameters dynamically • Performance evaluations with our benchmarks over different interconnects on modern clusters Performance Analysis and Evaluation of InfiniBand FDR and 40GigE RoCE on HPC and Cloud Computing Systems Problem statement • No study has been done to address the following questions: • How much benefit can the user of a HPC / Cloud installation hope to see by utilizing IB FDR / RoCE 40 GigE over IB QDR and RoCE 10 GigE interconnects, respectively? • How does InfiniBand compare with RoCE in terms of performance? Experimental Setup • Hardware • 16 cores/node – 2 Intel Sandy Bridge-EP 2.6 Ghz CPUs • 32 GB main memory, 20 MB L3 shared cache • 1 PCIe Gen3 (128 Gbps) Performance Results • Network Level Performance • Latency • MPI Level Performace • Point to point MPI • MPI Collectives • NAS Parallel Benchmarks • Cloud Computing Middlewares • HDFS write using TestDFSIO Latency Point to point MPI : Latency Point to point MPI: Bandwidth MPI Collective : Scatter NAS Parallel Benchmarks Class C HDFS Write Operation Conclusion • Carried out a comprehensive performance evaluation of four possible modes of communication • Latest InfiniBand FDR interconnect gives the best performance • Network level evaluations and for HPC applications: RoCE 40 GigE performance better than IB QDR • Cloud computing middleware: IPoIB QDR performance better than RoCE 40 GigE
© Copyright 2025 ExpyDoc
