22 research outputs found
Performance Characterization of In-Memory Data Analytics on a Modern Cloud Server
In last decade, data analytics have rapidly progressed from traditional
disk-based processing to modern in-memory processing. However, little effort
has been devoted at enhancing performance at micro-architecture level. This
paper characterizes the performance of in-memory data analytics using Apache
Spark framework. We use a single node NUMA machine and identify the bottlenecks
hampering the scalability of workloads. We also quantify the inefficiencies at
micro-architecture level for various data analysis workloads. Through empirical
evaluation, we show that spark workloads do not scale linearly beyond twelve
threads, due to work time inflation and thread level load imbalance. Further,
at the micro-architecture level, we observe memory bound latency to be the
major cause of work time inflation.Comment: Accepted to The 5th IEEE International Conference on Big Data and
Cloud Computing (BDCloud 2015
Memory and Parallelism Analysis Using a Platform-Independent Approach
Emerging computing architectures such as near-memory computing (NMC) promise
improved performance for applications by reducing the data movement between CPU
and memory. However, detecting such applications is not a trivial task. In this
ongoing work, we extend the state-of-the-art platform-independent software
analysis tool with NMC related metrics such as memory entropy, spatial
locality, data-level, and basic-block-level parallelism. These metrics help to
identify the applications more suitable for NMC architectures.Comment: 22nd ACM International Workshop on Software and Compilers for
Embedded Systems (SCOPES '19), May 201
NMPO:Near-Memory Computing Profiling and Offloading
Real-world applications are now processing big-data sets, often bottlenecked by the data movement between the compute units and the main memory. Near-memory computing (NMC), a modern data-centric computational paradigm, can alleviate these bottlenecks, thereby improving the performance of applications. The lack of NMC system availability makes simulators the primary evaluation tool for performance estimation. However, simulators are usually time-consuming, and methods that can reduce this overhead would accelerate the early-stage design process of NMC systems. This work proposes Near-Memory computing Profiling and Offloading (NMPO), a high-level framework capable of predicting NMC offloading suitability employing an ensemble machine learning model. NMPO predicts NMC suitability with an accuracy of 85.6% and, compared to prior works, can reduce the prediction time by using hardware-dependent applications features by up to 3 order of magnitude
Near Memory Acceleration on High Resolution Radio Astronomy Imaging
Modern radio telescopes like the Square Kilometer Array (SKA) will need to
process in real-time exabytes of radio-astronomical signals to construct a
high-resolution map of the sky. Near-Memory Computing (NMC) could alleviate the
performance bottlenecks due to frequent memory accesses in a state-of-the-art
radio-astronomy imaging algorithm. In this paper, we show that a sub-module
performing a two-dimensional fast Fourier transform (2D FFT) is memory bound
using CPI breakdown analysis on IBM Power9. Then, we present an NMC approach on
FPGA for 2D FFT that outperforms a CPU by up to a factor of 120x and performs
comparably to a high-end GPU, while using less bandwidth and memory
Hauptsätze der Differential- und Integral-Rechnung : als Leitfaden zum Gebrauch bei Vorlesungen / zusammengestellt von Robert Fricke ; 1. Theil
\u3cp\u3eThe conventional approach of moving data to the CPU for computation has become a significant performance bottleneck for emerging scale-out data-intensive applications due to their limited data reuse. At the same time, the advancement in 3D integration technologies has made the decade-old concept of coupling compute units close to the memory — called near-memory computing (NMC) — more viable. Processing right at the “home” of data can significantly diminish the data movement problem of data-intensive applications. In this paper, we survey the prior art on NMC across various dimensions (architecture, applications, tools, etc.) and identify the key challenges and open issues with future research directions. We also provide a glimpse of our approach to near-memory computing that includes i) NMC specific microarchitecture independent application characterization ii) a compiler framework to offload the NMC kernels on our target NMC platform and iii) an analytical model to evaluate the potential of NMC.\u3c/p\u3
Project Night-King:Â Improving the performance of big data analytics using Near Data Computing Architectures
The goal of Project Night-King is to improve the single-node performance of scale-out big data processing frameworks like Apache Spark using programmable accelerators near DRAM and NVRAM. Using modeling techniques, we estimate the lower bound of 5x performance improvement for Spark MLlib workloads.QC 20171031</p