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UID:submissions.supercomputing.org_SC20_sess201@linklings.com
SUMMARY:IA^3 2020: 10th Workshop on Irregular Applications: Architectures
and Algorithms
DESCRIPTION:Workshop\n\nIA^3 2020 – Lunch Break\n\n\n\n-------------------
--\nLabeled Triangle Indexing for Efficiency Gains in Distributed Interact
ive Subgraph Search\n\nReza, Ripeanu, Sanders, Pearce\n\nSubgraph search i
n a massive background graph, i.e., pattern matching in graphs, is a chall
enging problem, particularly in an interactive usage scenario where fast r
esponse time is important. Our approach, PruneJuice, is based on two intu
itions: rather than directly searching for individual matches...\n\n------
---------------\nIA^3 2020 – Break\n\n\n\n---------------------\nIA^3 2020
– Keynote: Memory Performance Optimization\n\nJayasena\n\nMany of the imp
lementation technology scaling trends the computing industry has historica
lly relied on have started to taper off or are posing increasing design ch
allenges. This has led to the proliferation of many-core processors and ac
celerators, advanced packaging technologies, and innovations in...\n\n----
-----------------\nDistributed Memory Graph Coloring Algorithms for Multip
le GPUs\n\nBogle, Boman, Devine, Rajamanickam, Slota\n\nGraph coloring is
often used in parallelizing scientific computations that run in distribute
d and multi-GPU environments; it identifies sets of independent data that
can be updated in parallel. Many algorithms exist for graph coloring on a
single GPU or in distributed memory, but hybrid MPI+GPU algo...\n\n-------
--------------\nIA^3 2020 – Thank You and Closing\n\nTumeo, Castellana, Fe
o\n\n---------------------\nPerformance Evaluation of the Vectorizable Bin
ary Search Algorithms on an FPGA Platform\n\nJin, Finkel\n\nField-programm
able gate arrays (FPGAs) are becoming promising heterogeneous computing co
mponents. In the meantime, high-level synthesis (HLS) tools are pushing th
e FPGA-based development from the register-transfer level to high-level-la
nguage design flow using Open Computing Language (OpenCL), C, an...\n\n---
------------------\nSupporting Irregularity in Throughput-Oriented Computi
ng by SIMT-SIMD Integration\n\nThuerck\n\nThe last two decades have seen c
ontinued exponential performance increases in HPC systems, well after the
predicted end of Moore's Law for CPUs, largely due to the widespread adopt
ion of throughput-oriented compute accelerators such as GPUs. When faced w
ith irregular yet throughput-oriented applicat...\n\n---------------------
\nIA^3 2020 – Paper Session – Q/A\n\nTumeo, Sofranac, Solis-Vasquez, Timch
eck, Thuerck\n\n---------------------\nAccelerating Domain Propagation: an
Efficient GPU-Parallel Algorithm over Sparse Matrices\n\nSofranac, Gleixn
er, Pokutta\n\nFast domain propagation of linear constraints has become a
crucial component of today’s best algorithms and solvers for mixed integer
programming and pseudo-boolean optimization to achieve peak solving perfo
rmance. Irregularities in the form of dynamic algorithmic behavior, depend
ency structures, an...\n\n---------------------\nIA^3 2020 – Introduction:
10th Workshop on Irregular Applications: Architectures and Algorithms\n\n
Tumeo, Feo, Castellana\n\nDue to the heterogeneous data sets they process,
data intensive applications employ a diverse set of methods and data stru
ctures, exhibiting irregular memory accesses, control flows and communicat
ion patterns. Current supercomputing systems are organized around componen
ts optimized for data locality...\n\n---------------------\nDistDGL: Distr
ibuted Graph Neural Network Training for Billion-Scale Graphs\n\nZheng, Ma
, Wang, Zhou, Su...\n\nGraph neural networks (GNN) have shown great succes
s in learning from graph-structured data. They are widely used in various
applications, such as recommendation, fraud detection, and search. In the
se domains, the graphs are typically large, containing hundreds of million
s of nodes and several bill...\n\n---------------------\nReducing Queuing
Impact in Irregular Data Streaming Applications\n\nTimcheck, Buhler\n\nThr
oughput-oriented streaming applications on massive data sets are a prime c
andidate for parallelization on wide-SIMD platforms, especially when input
s are independent of one another. Many such applications are represented a
s a pipeline of compute nodes connected by directed edges. Here, we study
a...\n\n---------------------\nIA^3 2020 – Break\n\n\n\n------------------
---\nIA^3 2020 – Keynote: Research Challenges in Compiler Technology for S
parse Tensors\n\nHall\n\nScalable computations where the data is sparse —
that is, a tiny subset of the data is populated — are widely represented i
n scientific computing, data analytics and machine learning. Sparse data
are typically represented by sparse matrices and graphs, which reduce data
storage and computation requ...\n\n---------------------\nParallelizing I
rregular Computations for Molecular Docking\n\nSolis-Vasquez, Santos-Marti
ns, Tillack, Koch, Eberhardt...\n\nAUTODOCK is a molecular docking softwar
e widely used in computational drug design. Its time-consuming executions
have motivated the development of AUTODOCK-GPU, an OpenCL-accelerated vers
ion that can run on GPUs and CPUs. This work discusses the development of
AUTODOCK-GPU from a programming perspec...\n\n---------------------\nIA^3
2020 – Paper Session: Q/A\n\nTumeo, Zheng, Reza, Bogle, Jin\n\n-----------
----------\nIA^3 2020 – Break\n\n\n\n---------------------\nIA^3 2020 - Pa
nel\n\nCastellana, Beamer, Becchi, Bonifati, Pearce...\n\n\nRegistration C
ategory: Workshop Reg Pass
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