8135

A Map-Reduce-Like System for Programming and Optimizing Data-Intensive Computations on Emerging Parallel Architectures

Wei Jiang
The Ohio State University
The Ohio State University, 2012
@phdthesis{jiang2012map,

   title={A Map-Reduce-Like System for Programming and Optimizing Data-Intensive Computations on Emerging Parallel Architectures},

   author={Jiang, W.},

   year={2012},

   school={The Ohio State University}

}

Download Download (PDF)   View View   Source Source   

632

views

Parallel computing environments are ubiquitous nowadays, including traditional CPU clusters and the emergence of GPU clusters and CPU-GPU clusters because of their performance, cost and energy efficiency. With this trend, an important research issue is to effectively utilize the massive computing power in these architectures to accelerate data-intensive applications arising from commercial and scientific domains. Map-reduce and its Hadoop implementation have become popular for its high programming productivity but exhibits non-trivial performance losses for many classes of data-intensive applications. Also, there is no general map-reduce-like support up to date for programming heterogeneous systems like a CPU-GPU cluster. Besides, it is widely accepted that the existing fault tolerant techniques for high-end systems will not be feasible in the exascale era and novel solutions are clearly needed. Our overall goal is to solve these programmability and performance issues by providing a map-reduce-like API with better performance efficiency as well as efficient fault tolerance support, targeting data-intensive applications and various new emerging parallel architectures. We believe that a map-reduce-like API can ease the programming difficulty in these parallel architectures, and more importantly improve the performance efficiency of parallelizing these data-intensive applications. Also, the use of a high-level programming model can greatly simplify fault-tolerance support, resulting in low overhead checkpointing and recovery. We performed a comparative study showing that the map-reduce processing style could cause significant overheads for a set of data mining applications. Based on the observation, we developed a map-reduce system with an alternate API (MATE) using a user-declared reduction-object to be able to further improve the performance of map-reduce programs in multi-core environments. To address the limitation in MATE that the reduction object must fit in memory, we extended the MATE system to support the reduction object of arbitrary sizes in distributed environments and apply it to a set of graphmining applications, obtaining better performance than the original graph mining library based on map-reduce. We then supported the generalized reduction API in a CPU-GPU cluster with the ability of automatic data distribution among CPUs and GPUs to achieve the best-possible heterogeneous execution of iterative applications. Finally, in our recent work, we extended the generalized reduction model with supporting low overhead fault tolerance for MPI programs in our FT-MATE system. Especially, we are able to deal with CPU/GPU failures in a cluster with low overhead checkpointing, and restart the computations from a different number of nodes. Through this work, we would like to provide useful insights for designing and implementing efficient fault tolerance solutions for the exascale systems in the future.
VN:F [1.9.22_1171]
Rating: 4.7/5 (3 votes cast)
A Map-Reduce-Like System for Programming and Optimizing Data-Intensive Computations on Emerging Parallel Architectures, 4.7 out of 5 based on 3 ratings

* * *

* * *

Like us on Facebook

HGPU group

192 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1329 peoples are following HGPU @twitter

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: