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_collections/_updates/2023-11-29-introducing-the-syclops-consortium.md
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title: "Introducing the SYCLOPS Consortium" | ||
date: 2023-11-29 | ||
layout: update | ||
tags: | ||
- consortium | ||
- announcement | ||
--- | ||
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Announced in May this year, the SYCLOPS Project brings together 8 leading organizations from across Europe, | ||
collaborating to democratize AI acceleration and break the existing monopoly on the acceleration market. Our combined | ||
vision is using open standards to enable a healthy, competition-driven ecosystem with widespread adoption of both | ||
RISC-V®, an open standard Instruction Set Architecture (ISA) and SYCL™, the open specification parallel to | ||
ISO C++ and maintained by the Khronos Group. | ||
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Each collaborating partner brings their own expertise and unique insights to the project, and in this blog each member | ||
of the SYCLOPS project will introduce their organization, and the work they will be contributing to the project. | ||
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## Accelom | ||
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ACCELOM is a French SME specializing in the development of bespoke AI-based multi-omics pipelines for performing | ||
integrated analysis of genomic data from multiple molecular levels to provide a holistic picture of how genomics drives | ||
the progression of a disease in a way that would not be possible using traditional, non-integrated analysis methods. One | ||
key challenge faced by ACCELOM is that its pipelines are designed for a scale-up, single-server, CPU-based deployment as | ||
it is intended for its customers and collaborators who cannot move their data to the public cloud due to privacy | ||
reasons, and who cannot afford to operate large datacenters due to economic constraints. Through project SYCLOPS, | ||
ACCELOM will develop SYCL-GAL and use it to develop a new version of the multiomics pipeline. We will demonstrate the | ||
effectiveness of the new pipeline via thorough, extensive evaluation using publicly available datasets from European | ||
data sources | ||
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## CERN | ||
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The European Organization for Nuclear Research (CERN) was established in 1954 near Geneva, Switzerland. At CERN, high | ||
energy physics collaborations conduct experiments with the help of particle accelerators. CERN currently operates the | ||
world's most powerful particle accelerator, the Large Hadron Collider (LHC). The ROOT project at CERN provides software | ||
libraries and tools for the processing of the ever growing amount of data collected from the LHC. In order to sustain | ||
this data deluge the software needs to be ported to computing accelerators. ROOT provides use cases for the SYCLOPS | ||
project, porting them to SYCL and measuring their performance. | ||
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## Codasip | ||
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Codasip is a processor technology company enabling SoC developers to differentiate their products for competitive | ||
advantage. Codasip’s offering can be summarized as Custom Compute. The solution is based on the Codasip Studio design | ||
automation tools. The tools can be used to customize and optimize the company’s range of RISC-V processor cores by | ||
adding domain-specific or application-specific extensions, or to design a processor from scratch. Codasip’s role in the | ||
project is to develop domain-specific accelerators based on the RISC-V Vector extension (RVV). This will be achieved | ||
using Codasip Studio and the CodAL processor description language. | ||
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## Codeplay® | ||
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Codeplay Software, founded in 2002 in Edinburgh, Scotland is a leader in acceleration technologies used in the AI and | ||
HPC industry. In 2022, they were acquired by Intel to expand the oneAPI ecosystem based on SYCL, a cross-platform | ||
software abstraction for heterogeneous processors. As active members of the Khronos Group and leaders in the open | ||
standards ecosystem, Codeplay will use their expertise to lead on the communication activities of SYCLOPS and | ||
participate in conferences and events to showcase the ongoing work of the project. | ||
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## Eurecom | ||
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EURECOM is a leading French engineering school and a founding member of the SophiaTech Campus in Sophia Antipolis, the | ||
largest Computer Science and Technology campus in Alpes-Maritimes region of France. The Data Science department of | ||
EURECOM brings its world-renowned expertise and reputation in the field of statistical machine learning, multimodal data | ||
processing and knowledge engineering, which empower artificial intelligence systems. The Data Science department is a | ||
founding member of the “Region SUD” Artificial Intelligence cluster, which was recently recognized by the French | ||
government as a cluster of excellence (3IA). In SYCLOPS, EURECOM acts as both the technical and administrative | ||
coordinator. In addition, EURECOM will also collaborate with various SYCLOPS partners in developing optimized algorithms | ||
for scaling the performance of various cross-architecture acceleration libraries. | ||
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## Heidelberg University | ||
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Heidelberg University – also known as Ruperto Carola – was established in 1386 and is Germany’s oldest university. It is | ||
also one of the strongest research universities in all of Europe. The successes of Heidelberg University in the | ||
Excellence Competitions and in international rankings are evidence of its leading role on the academic scene. | ||
Heidelberg University is a comprehensive university with a broad spectrum of subjects and focus on research-based | ||
teaching and well-structured training for doctoral students. | ||
Currently, approx. 1.400 Postdocs and 8.900 PhD Students are enrolled and 36 ERC grants are running at Heidelberg | ||
University. | ||
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The Engineering Mathematics and Computing Lab (EMCL) is a research group at the Interdisciplinary Center for Scientific | ||
Computing (IWR) at Heidelberg University. EMCL is particularly noted for its openness to multidisciplinary research in | ||
the field of engineering and scientific computing. EMCL currently pursues five major research themes: Computational | ||
Mathematics, Uncertainty Quantification, Hardware Aware Numerics and High Performance Computing and IT Security. | ||
One of EMCL’s developments, AdaptiveCpp (formerly known as hipSYCL), is a flexible compiler and runtime system that | ||
implements the SYCL standard on multiple device architectures. In the course of the project, EMCL will expand | ||
AdaptiveCpp to support for RISC-V devices. This requires generalizing and extending the existing compiler and runtime | ||
infrastructure. Additionally, EMCL will work on techniques to increase the utilization of devices in the system by | ||
leveraging automatic work distribution. As a provider of a developer tool at the platform layer of the SYCLOPS stack, | ||
EMCL will work closely with the project partners to ensure that AdaptiveCpp is a reliable platform for the project. | ||
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## Hiro Microdatacenters | ||
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HIRO-MicroDataCenters is a Dutch micro SME specializing in Edge as a Service, specifically MicroDataCenter solutions ( | ||
hardware and software) for Big Data processing and AI in data dense Edge environments in various Industries & | ||
applications. In SYCLOPS, HIRO will be developing high-performance, energy-efficient, heterogeneous edge micro | ||
datacenters (EMDC) that will serve as both development testbed to validate the SYCLOPS stack and as a reference platform | ||
for the design of future PCIe 5.0 compatible EMDCs in comExpress form factor. | ||
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## INESC-ID | ||
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INESC-ID (Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento em Lisboa) is a private, | ||
not-for-profit Portuguese R&D and Innovation organization that promotes cooperation between academia and industry and | ||
has been establishing excellence in R&D activities since 1999. INESC-ID now stands, and is viewed, as one of the most | ||
dynamic research institutes in Portugal in the broad areas of computer science and electrical engineering. The research | ||
relevance of the INESC-ID High-Performance Computing Architectures and Systems (HPCAS) group on performance modeling, | ||
application optimization, and parallelization on state-of-the-art computing platforms synergies among the objectives of | ||
the SYCLOPS project. In this regard, INESC-ID’s role in the project spans multiple layers of the SYCLOPS stack, which | ||
include the exploration of cross-architecture insightful performance models, profiling of the three SYCLOPS use cases, | ||
as well as enabling acceleration strategies within the SYCL-GAL library with SYCL-based version of pairHMM used for | ||
variant calling. |
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