Seminário de Arquitetura

[Rodolfo Azevedo]

Próxima segunda: 24/11 - 10h - Sala 351

Gennady Pekhimenko, Vivek Seshadri, Yoongu Kim, Hongyi Xin, Onur Mutlu, Phillip B. Gibbons, Michael A. Kozuch, and Todd C. Mowry. 2013. Linearly compressed pages: a low-complexity, low-latency main memory compression framework. In Proceedings of the 46th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO-46). ACM, New York, NY, USA, 172-184. DOI=10.1145/2540708.2540724

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Rodolfo Jardim de Azevedo
http://www.ic.unicamp.br/~rodolfo
IC - University of Campinas - UNICAMP

[Rodolfo Azevedo]

Próxima segunda, 01/12, 10h, sala 351

TITLE: Hybrid Programming Challenges for Extreme Scale Software

SPEAKER: Professor Vivek Sarkar, E.D. Butcher Chair in Engineering, Rice University

ABSTRACT:

It is widely recognized that computer systems in the next decade will

be qualitatively different from current and past computer systems.

Specifically, they will be built using homogeneous and heterogeneous

many-core processors with 100's of cores per chip, their performance

will be driven by parallelism (million-way parallelism just for a

departmental server), and constrained by energy and data movement.

They will also be subject to frequent faults and failures.  Unlike

previous generations of hardware evolution, these Extreme Scale

systems will have a profound impact on future software.  The software

challenges are further compounded by the need to support new workloads

and application domains that have traditionally not had to worry about

parallel computing in the past.

In general, a holistic redesign of the entire software stack is needed

to address the programmability and performance requirements of Extreme

Scale systems.  This redesign will need to span programming models,

languages, compilers, runtime systems, and system software.  A major

challenge in this redesign arises from the fact that current

programming systems have their roots in execution models that focused

on homogeneous models of parallelism e.g., OpenMP and Java's roots are

in SMP parallelism, MPI and Hadoop's roots are in cluster parallelism,

and CUDA and OpenCL's roots are in GPU parallelism.  This in turn

leads to the "hybrid programming" challenge for application

developers, as they are forced to explore approaches to combine two or

all three of these models in the same application.  Despite some early

experiences and attempts by some of the programming systems to broaden

their scope (e.g., addition of accelerator pragmas to OpenMP), hybrid

programming remains an open problem and a major obstacle for

application enablement on future systems.

In this talk, we summarize experiences with hybrid programming in the

Habanero Extreme Scale Software Research project [1] which targets a

wide range of homogeneous and heterogeneous manycore processors in

both single-node and cluster configurations.  We focus on key

primitives in the Habanero execution model that simplify hybrid

programming, while also enabling a unified runtime system for

heterogeneous hardware.  Some of these primitives are also being

adopted by the new Open Community Runtime (OCR) open source project

[2].  These primitives for hybrid programming have been validated in a

range of applications studied in the NSF Expeditions Center for

Domain-Specific Computing (CDSC) [3], which include medical imaging

applications and Hadoop-style parallelization of machine learning

algorithms.

Background material for this talk will be drawn in part from the DARPA

Exascale Software Study report [4] led by the speaker.  This talk will

also draw from a recent (March 2013) study led by the speaker on

Synergistic Challenges in Data-Intensive Science and Exascale

Computing [5] for the US Department of Energy's Office of Science.  We

would like to acknowledge the contributions of all participants in

both studies, as well as the contributions of all members of the

Habanero, OCR, and CDSC projects.

BIO:

Vivek Sarkar conducts research in multiple aspects of parallel

software including programming languages, program analysis, compiler

optimizations and runtimes for parallel and high performance computer

systems.  He currently leads the Habanero Extreme Scale Software Research

project at Rice University, and serves as Associate Director of the

NSF Expeditions project on the Center for Domain-Specific Computing.

Prior to joining Rice in July 2007, Vivek was Senior Manager of

Programming Technologies at IBM Research.  His responsibilities at IBM

included leading IBM's research efforts in programming model, tools,

and productivity in the PERCS project during 2002- 2007 as part of the

DARPA High Productivity Computing System program.  His past projects

include the X10 programming language, the Jikes Research Virtual

Machine for the Java language, the MIT RAW multicore project, the ASTI

optimizer used in IBM's XL Fortran product compilers, the PTRAN

automatic parallelization system, and profile-directed partitioning

and scheduling of Sisal programs.  Vivek holds a B.Tech. degree from

the Indian Institute of Technology, Kanpur, an M.S. degree from

University of Wisconsin-Madison, and a Ph.D. from Stanford University.

He became a member of the IBM Academy of Technology in 1995, the

E.D. Butcher Chair in Engineering at Rice University in 2007, and was

inducted as an ACM Fellow in 2008.  Vivek has been serving as a member

of the US Department of Energy's Advanced Scientific Computing

Advisory Committee (ASCAC) since 2009.  He has also become the chair

of the Computer Science Department at Rice University since July 2013.

REFERENCES:

[1] Habanero Extreme Scale Software Research project.  http://habanero.rice.edu.

[2] Open Community Runtime (OCR) open source project.  https://xstackwiki.modelado.org/Open_Community_Runtime.

[3] Center for Domain-Specific Computing (CDSC).  http://cdsc.ucla.edu.

[4] DARPA Exascale Software Study report, September 2009.  http://users.ece.gatech.edu/~mrichard/ExascaleComputingStudyReports/ECS_reports.htm.

[5] DOE report on Synergistic Challenges in Data-Intensive Science and Exascale Computing, March 2013. http://science.energy.gov/~/media/ascr/ascac/pdf/reports/2013/ASCAC_Data_Intensive_Computing_report_final.pdf.