NDLI: Building Fuel Powered Supercomputing Data Center at Low Cost

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Streaming Task Parallelism

Automatic Parallelization of Kernels in Shared-Memory Multi-GPU Nodes

PaCMap: Topology Mapping of Unstructured Communication Patterns onto Non-contiguous Allocations

Locality-Driven Dynamic GPU Cache Bypassing

Automatic Selection of Sparse Matrix Representation on GPUs

Datacenter Efficiency: What's Next?

Towards Lightweight and Swift Storage Resource Management in Big Data Cloud Era

MODESTO: Data-centric Analytic Optimization of Complex Stencil Programs on Heterogeneous Architectures

GreenPar: Scheduling Parallel High Performance Applications in Green Datacenters

History-Assisted Adaptive-Granularity Caches (HAAG\$) for High Performance 3D DRAM Architectures

Automatically Scalable Computation

Mower: A New Design for Non-blocking Misprediction Recovery

PALMOS: A Transparent, Multi-tasking Acceleration Layer for Parallel Heterogeneous Systems

CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication

Automatic Energy Efficient Parallelization of Uniform Dependence Computations

COMPASS: A Framework for Automated Performance Modeling and Prediction

A Stall-Aware Warp Scheduling for Dynamically Optimizing Thread-level Parallelism in GPGPUs

Unique Worker model for OpenMP

SemCache++: Semantics-Aware Caching for Efficient Multi-GPU Offloading

Fine-Grained Synchronizations and Dataflow Programming on GPUs

Hadoop+: Modeling and Evaluating the Heterogeneity for MapReduce Applications in Heterogeneous Clusters

FAST: A Fast Stencil Autotuning Framework Based On An Optimal-solution Space Model

Underprovisioning the Grid Power Infrastructure for Green Datacenters

Real-Time In-Memory Checkpointing for Future Hybrid Memory Systems

zFENCE: Data-less Coherence for Efficient Fences

A Nested Partitioning Algorithm for Adaptive Meshes on Heterogeneous Clusters

Optimistic Delinearization of Parametrically Sized Arrays

ASPaS: A Framework for Automatic SIMDization of Parallel Sorting on x86-based Many-core Processors

Composing Algorithmic Skeletons to Express High-Performance Scientific Applications

PeerWave: Exploiting Wavefront Parallelism on GPUs with Peer-SM Synchronization

Exploiting Process Imbalance to Improve MPI Collective Operations in Hierarchical Systems

DaCache: Memory Divergence-Aware GPU Cache Management

Enabling and Exploiting Flexible Task Assignment on GPU through SM-Centric Program Transformations

Active Access: A Mechanism for High-Performance Distributed Data-Centric Computations

Parameterized Diamond Tiling for Stencil Computations with Chapel parallel iterators

Building Fuel Powered Supercomputing Data Center at Low Cost

Leveraging Silicon-Photonic NoC for Designing Scalable GPUs

Criticality-Aware Dynamic Task Scheduling for Heterogeneous Architectures

DASX: Hardware Accelerator for Software Data Structures

Optimizing Overlapped Memory Accesses in User-directed Vectorization

STAPL-RTS: An Application Driven Runtime System

Exascaling Your Library: Will Your Implementation Meet Your Expectations?

Quantifying Performance Bottlenecks of Stencil Computations Using the Execution-Cache-Memory Model

Building Fuel Powered Supercomputing Data Center at Low Cost

Content Provider	ACM Digital Library
Author	Liang, Xiaoyao Jiang, Li Hua, Yiqing Li, Chao Tang, Weichao
Abstract	Distributed power generations that fed with various economical clean fuels are emerging as promising power supplies for extremescale computing systems. Recent years have witnessed a growing adoption of these non-conventional power supplies in data center designs due to the heightening demand for reducing IT carbon footprint and server energy cost. However, the benefits of such a fuel powered data center are often severely compromised by its high initial capital cost (CapEx). This is because most pilot designs today either rely on expensive advanced generators or employ low-performance generators with costly standby power backup. In this study we exploit heterogeneous generation to reduce the cost of data center powered by fuel. We show that different types of power supplies, if used together, can greatly improve the cost-effectiveness of self-generation but introduce a new layer of design complexity and raise an important question of how to dispatch computing tasks on heterogeneous power supplies. Specifically, due to the non-ideal output power response speed of heterogeneous generators, servers may incur serious power budget deficiencies when dispatching large amount of jobs. We refer to this phenomenon as power lagging, which jeopardizes system reliability and are not economical to be handled by costly power backup systems. To overcome this barrier, we propose Batch, an agile load dispatching scheme that eliminate power lagging at the system/software level. Other than dispatch computing tasks in bulk without considering power system behaviors, Batch intelligently splits job queue into small sets and incrementally schedule jobs based on the power ramping rate constraints and total power budget constraints. Using realistic HPC datacenter load traces, we demonstrate that Batch enables supercomputers to smoothly operate on heterogeneous power. Our design helps data center operators save over 80% cost while maintaining the desired workload performance.
Starting Page	241
Ending Page	250
Page Count	10
File Format	PDF
ISBN	9781450335591
DOI	10.1145/2751205.2751215
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Hybrid generation Fuel Data center Dispatching Cost
Content Type	Text
Resource Type	Article

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