Simon Garcia de Gonzalo simon.garcia at bsc.es
Wed Nov 17 06:24:53 CST 2021

(AsHES 2022)*




- Paper Submission: 30^TH JANUARY 2022

- Paper Notification: 6^TH  MARCH 2022

Collocated with IPDPS2022 (https://www.ipdps.org/)



The current computing landscape has gone through an ever-increasing rate 
of change and innovation. This change has been driven by the relentless 
need to improve the energy-efficient, memory, and compute throughput at 
all levels of the architectural hierarchy. Although the amount of data 
that has to be organized by today's systems posed new challenges to the 
architecture, which can no longer be solved with classical, homogeneous 
design. Improvements in all of those areas have led Heterogeneous 
systems to become the norm rather than the exception.

Heterogeneous computing leverages a diverse set of computing (CPU, GPU, 
FPGA, TPU ...) and Memory (HBM, Persistent Memory, Coherent PCI 
protocols, etc ..), hierarchical storage systems and units to accelerate 
the execution of a diverse set of applications. Emerging and existing 
areas such as AI, BigData, Cloud Computing, Edge-Computing, Real-time 
systems, High-Performance Computing, and others have seen a real benefit 
due to Heterogenous computer architectures. These new heterogeneous 
architectures often also require the development of new applications and 
programming models, in order to satisfy these new architectures and to 
fully utilize these capacities. This workshop focuses on understanding 
the implications of heterogeneous designs at all levels of the computing 
system stack, such as hardware, compiler optimizations, porting of 
applications, and developing programming environments for current and 
emerging systems in all the above-mentioned areas. It seeks to ground 
heterogeneous system design research through studies of application 
kernels and/or whole applications, as well as shed light on new tools, 
libraries and runtime systems that improve the performance and 
productivity of applications on heterogeneous systems.

The goal of this workshop is to bring together researchers and 
practitioners who are at the forefront of Heterogeneous computing in 
order to learn the opportunities and challenges in future Heterogeneous 
system design trends and thus help influence the next trends in this area.

Topics of interest for the *full paper* (8 - 10 pages) track submissions 
include (but are not limited to):

- Applications for hybrid/heterogeneous systems.

- Innovative use of heterogeneous computing in AI for science or 
optimizations for AI

- Heterogeneous computing at Edge

- Design and use of domain-specific functionalities on accelerators

- Strategies for programming heterogeneous systems using high-level 
models such as OpenMP,   OpenACC, SYCL, low-level models such as OpenCL, 

- Methods and tools to tackle challenges from heterogeneity in AI/ML/DL, 
BigData, Cloud Computing, Edge-Computing, Real-time Systems, and 
High-Performance Computing;

- Strategies for application behavior characterization and performance 
optimization for accelerators;

- Techniques for optimizing kernels for execution on GPGPU, FPGA, TPU, 
and emerging heterogeneous platforms;

- Models of application performance on heterogeneous and accelerated HPC 

- Compiler Optimizations and tuning heterogeneous systems including 
parallelization, loop transformation, locality optimizations, Vectorization;

- Implications of workload characterization in heterogeneous and 
accelerated architecture design;

- Benchmarking and performance evaluation for heterogeneous systems at 
all level of the system stack;

- Tools and techniques to address both performance and correctness to 
assist application development for accelerators and heterogeneous 

- System software techniques to abstract application domain-specific 
functionalities for accelerators;

A *short paper* track (maximum of 4 pages) has been added to this year’s 
program to highlight early investigations of innovative ideas in 
emerging selected topics such as:

- Innovative use of heterogeneous computing in AI for science or 
optimizations for AI

- Heterogeneous computing at Edge

- Design and use of domain-specific functionalities on accelerators

- Hybrid neuromorphic computing systems

- In-memory architectures.


Program Chairs

Simon Garcia de Gonzalo, Barcelona Supercomputing Center (BSC)

Shintaro Iwasaki, Facebook AI, USA

General Chair

Lena Oden, FernUni Hagen

Simon Garcia de Gonzalo
Postdoctoral Reasearcher
Accelerators and Communications for High Performance Computing
Barcelona Supercomputing Center (BSC)

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