2015 Proceeding- General Chair:
- Philip C. Roth
Welcome to DISCS-2015! The Data Intensive Scalable Computing Systems (DISCS) workshop series facilitates dialogue about research aimed at the intersection of data intensive computing and traditional high performance computing (HPC). Traditional HPC systems were designed from a compute-centric perspective, with an emphasis on high floating-point performance. As scientific and analytics applications become more data intensive, there is a need to rethink HPC system architectures, programming models, runtime systems, and tools with a focus on data intensive computing. Industry approaches supporting data intensive applications have been highly successful, leading many in the HPC community to explore ways to apply them. Conversely, the HPC community's expertise in designing, deploying, and using high performance systems is attractive to those in industry. The 2015 International Workshop on Data Intensive Scalable Computing Systems provides a forum for researchers and other interested people in the areas of data intensive computing and high performance parallel computing to exchange ideas and discuss approaches for addressing the challenges facing Big Data or data intensive computing at large scale.
Proceeding Downloads
SJM: an SCM-based journaling mechanism with write reduction for file systems
Considering the unique characteristics of storage class memory (SCM), such as non-volatility, fast access speed, byte-addressability, low-energy consumption, and in-place modification support, we investigated the features of over-write and append-write ...
Efficient disk-to-disk sorting: a case study in the decoupled execution paradigm
- Hassan Eslami,
- Anthony Kougkas,
- Maria Kotsifakou,
- Theodoros Kasampalis,
- Kun Feng,
- Yin Lu,
- William Gropp,
- Xian-He Sun,
- Yong Chen,
- Rajeev Thakur
Many applications foreseen for exascale era should process huge amount of data. However, the IO infrastructure of current supercomputing architecture cannot be generalized to deal with this amount of data due to the need for excessive data movement from ...
A low-cost adaptive data separation method for the flash translation layer of solid state drives
Solid state drives (SSDs) have shown great potential for data-intensive computing due to their much higher throughput and lower energy consumption compared to traditional hard disk drives. Within an SSD, its Flash Translation Layer (FTL) is responsible ...
Big data analytics on traditional HPC infrastructure using two-level storage
Data-intensive computing has become one of the major workloads on traditional high-performance computing (HPC) clusters. Currently, deploying data-intensive computing software framework on HPC clusters still faces performance and scalability issues. In ...
Route-aware independent MPI I/O on the blue gene/Q
Scalable high-performance I/O is crucial for application performance on large-scale systems. With the growing complexity of the system interconnects, it has become important to consider the impact of network contention on I/O performance because the I/O ...
Experimental evaluation of a flexible I/O architecture for accelerating workflow engines in cloud environments
In the current scientific computing scenario storage systems are one of the main bottlenecks in computing platforms. This issue affects both traditional high performance computing systems and modern systems based on cloud platforms. Accelerating the I/O ...
A case study of MapReduce speculation for failure recovery
MapReduce has become indispensable for big data analytics. As a representative implementation of MapReduce, Hadoop/YARN strives to provide outstanding performance in terms of job turnaround time, fault tolerance etc. It is equipped with a speculation ...
Supporting online analytics with user-defined estimation and early termination in a MapReduce-like framework
Online analytics based on runtime approximation has been widely adopted for meeting time and/or resource constraints. Though MapReduce has been gaining its popularity in both scientific and commercial sectors, there are several obstacles in implementing ...
Performance evaluation and tuning of BioPig for genomic analysis
In this study, we aim to optimize Hadoop parameters to improve the performance of BioPig on Amazon Web Service (AWS). BioPig is a toolkit for large-scale sequencing data analysis and is built on Hadoop and Pig that enables easy parallel programming and ...
Index Terms
- Proceedings of the 2015 International Workshop on Data-Intensive Scalable Computing Systems
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| DISCS '15 | 15 | 9 | 60% |
| DISCS-2013 | 19 | 10 | 53% |
| Overall | 34 | 19 | 56% |