The objective of MCC is to bring together Nordic researchers and practitioners from academia and industry to present and discuss recent work in the area of multi-core computing. This year's edition is hosted by Blekinge Institute of Technology in Karlskrona, Sweden.

The scope of the workshop is both hardware and software aspects of multi-core computing, including design and development as well as practical usage of systems. The topics of interest include, but is not limited to, the following:

Architecture of multi-core processors, GPUs, accelerators, heterogeneous systems, memory systems, interconnects and on-chip networks
Parallel programming models, languages, environments
Parallel algorithms and applications
Compiler optimizations and techniques for multi-core systems
Hardware/software design trade-offs in multi-core systems
Operating system, middleware, and run-time system support for multi-core systems
Correctness and performance analysis of parallel hardware and software
Tools and methods for development and evaluation of multi-core systems

There are two types of papers eligible for submission. The first type is original research work and the second type is work already published in 2018 or later.

Participants submitting original work are asked to send an electronic version of the paper that does not exceed four pages using the ACM proceedings format, http://www.acm.org/publications/proceedings-template, to https://easychair.org/conferences/?conf=mcc20190.

The same URL is to be used should you want to present an already published paper as described above. In that case, you need to clearly specify that the paper is already published and where the paper has been published.

No proceedings will be distributed. Contributions will not disqualify subsequent publication in conferences or journals.

The conference web page is https://sites.google.com/view/mcc2019.

Important dates

Sep. 29 2019: Submission deadline
Oct. 27 2019: Author notification
Nov. 18 2019: Registration deadline
Nov. 27-28 2019: MCC Workshop

Hannes Pétur Eggertsson successfully defended his PhD thesis in Computer Science on GraphTyper: A pangenome method for identifying sequence variants at a population-scale. I had the honor to steer this defense in my role as vice head of faculty.

As you notice, only men are occurring here. We need to improve on this! More pictures can be found on flickr.

For evaluating implementations of the popular DBSCAN clustering algorithm, various publications use several datasets. Pointers to these datasets and information on paramaters (e.g. normalisation, epsilon and minpts) are collected here. You are welcome to contact me if you have further (big) datasets that are good benchmarks for DBSCAN.

Sarma et al.: μDBSCAN: An Exact Scalable DBSCAN Algorithm for Big Data Exploiting Spatial Locality

TODO: check in detail datasets used, but some are those datasets used in some of the other publications below, but "In addition, we have also used a few other real datasets: 3D Road Network (3DSRN) [32] contains vechicular GPS data; Household Power (HHP*) and KDDBIO145K (KDDB*) datasets are borrowed from UCI Repository [33]."

Gan, Tao: DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation

Data normalized to [0, 10^5 ] for every dimension.

MinPts = 100, Epsilon = 5000 and higher. (Note: far too high value turning almost the entire dataset into a single cluster -- the mis-claim is on their side!).

Their preprocessed datasets

• PAMAP2 (3,850,505 4D points),
• Farm (3,627,086 5D points),
• Houshold (2,049,280 7D points)

can be obtained from their webpage.

Mai, Assent, Jacobsen, Storgaard Dieu: Anytime parallel density-based clustering

• Same household datasets used as by Gan, Tao.
• Also PAMAP2 is used, but claimed to be 974,479 39D points whereas Gan and Tao reduced it to 4 dimensions using PCA, but claim to have 3,850,505 points.
• In addition, the UCI Gas Sensor dataset by Fonollosa et al. is used: 4,208,261 16D points (DETAILS NOT PROVIDED IN PAPER).

Kriegel, Schubert, Zimek: The (black) art of runtime evaluation: Are we comparing algorithms or implementations?

• Same PAMAP2, Farm and household datasets used as by Gan, Tao (including also smaller epsilon values as these make more sense).
• In addition, for higher dimensional data, the Amsterdam Library of Object Images (ALOI) dataset from Geusebroek et al is used, namely the 110250 HSV/HSB color histograms provided on the ELKI Multi-View Data Sets webpage. Namly, the eight dimensions (two divisions per HSV color component) dataset (I assume, this is the 2x2x2 dataset) with epsilon=0.01 and minPts=20.

Patwary, Satish, Sundaram, Manne, Habib, Dubey: Pardicle: parallel approximate density-based clustering

• Halo and galaxy formation datasets in astrophysics, taken from the database on Millennium Run described by Lemson and the Virgo Consortium. (Online access via wget to the data stored in a SQLdatabase is possible.) These datasets contain far more dimensions than used by Patwary et al., so it is unclear which subsets of the dimensions have been used (DETAILS NOT PROVIDED IN PAPER).
• 1,056M 8D points (eps=0.005, minPts=2): This MPAGalaxiesDeLucia2006a (md) dataset (attributed by Patwary et al. to De Lucia et al. The hierarchical formation of the brightest cluster galaxies) probably refers to the DeLucia2006adataset
• 1,015M 8D points (eps=0.005, minPts=2): This DGalaxiesBower2006a (db) dataset (attributed by Patwary et al. to Bower et al. Breaking the hierarchy of galaxy formation) probably refers to the Bower2006a dataset.
• 1,015M 8D points (eps=0.005, minPts=2): For this MPAGalaxiesBertone2007 (mb) dataset (attributed by Patwary et al. to Bertone et al. The recycling of gas and metals in galaxy formation: predictions of a dynamical feedback model), it is not obvious to which dataset from the database on Millennium Run it refers to.
• 761M 9D points (eps=0.001, minPts=2). This MPAHaloTreesMhalo (mm) dataset (attributed by Patwary et al. to Guo et al.; Galaxy formation in WMAP1 and WMAP7 cosmologies) probably refers to the MPAHalo dataset.
• 92M and 116M 10 D points (eps=20, minPts=5 and eps=50, minPts=2): generated using the IBM Quest synthetic data generator, however details of generation have not been documented, so it cannot be reproduced.

PDSDBSCAN

A subsampled version of the above Millenium Run dataset has also been used in the paper A new scalable parallel DBSCAN algorithm using the disjoint-set data structure by the same main author as Pardicle describing and evaluating PDSDBSCAN who published also a 50,000 10D point dataset used also in that paper.

Götz, Bodenstein, Riedel: HPDBSCAN: highly parallel DBSCAN

The Bremen 3D point cloud and Twitter 2D GPS locations are available as full and subsampled (small) datasets: DOI: 10.23728/b2share.7f0c22ba9a5a44ca83cdf4fb304ce44e (Note: the original publication refers to the dataset via a handle.net handle which does not work anymore).

• Twitter (dataset t): 16,602,137 2D points (eps=0.01, minPts=40). Note that this dataset contains some bogus artefacts (most likely Twitter spam with bogus GPS coordinates).
• Twitter small (dataset ts): 3,704,351 2D points (eps=0.01, minPts=40)
• Bremen (dataset b): 81,398,810 3D points (eps=100, minPts=10000)
• Bremen small (dataset bs): 2,543,712 2D points (eps=100, minPts=312)

Neukirchen: Elephant against Goliath: Performance of Big Data versus High-Performance Computing DBSCAN Clustering Implementations

The same Twitter small dataset as provided by Götz et al. has been used with the same parameters.

The DEEP-EST ("Dynamical Exascale Entry Platform - Extreme Scale Technologies") project is funded as part of the European Commission's Horizon 2020 ambitious Future and Emerging Technologies (FET) programme in order to create the blueprints of the next generation ("pre-exascale") supercomputer hardware and software.
The current goal in supercomputing is to reach exascale performance: a quintillion in American culture or a trillion in European culture or 10 to the power of 18 floating point arithmetic operations per second (FLOPS). These are needed to drive large-scale scientific simulations and big data analytics forward. Current supercomputers are able to achieve 0.2 exaFLOPS (or 200 petaFLOPS or 200 thousand teraFLOPS) (for comparison: if you have a very high-end personal computer, it's CPU can maybe compute half a teraFLOP).

Exascale computing is some sort of "wall", i.e. it is hard to reach it and in particular to go beyond anytime soon. While according Moore's law the number of transistors in a CPU doubles every two years, the performance of a CPU does not anymore double that fast (the transistors go into more cores and more caches). Currently, the only way to boost performance is to use not generic CPUs, but specialised "accelerators", e.g. graphical processors (GPUs), but also accelerators in other parts of a supercomputer, e.g. the network fabric that inter-connects the many CPU nodes of a supercomputer or the storage. DEEP-EST therefore suggest a Modular Supercomputing Architecture (MSA) where the supercomputer is composed of multiple modules, each being specialised in a particular domain, e.g. a GPU-heavy booster for computations that scale well and are suitable for GPUs, a "normal" CPU cluster module for applications that do not scale that well, a data analysis module having hardware specialised for machine learning.

Talking about accelerators: one of our project partners is CERN and the project meeting took place there: we were lucky enough that the Large Hadron Collider (LHC) and particle accelerator is currently in maintenance/upgrade phase, so we where able to see one of the detectors (when it is running, the collisions create lots of radiation). -- Find the human in the picture below:

DEEP-EST has reached the middle of the project duration and the first module, the CPU cluster module has been installed. Since an additional barrier in exascale computing is energy, which also means heat created by the computers that need to be cooled down, DEEP-EST is also working on novel cooling solutions, e.g. water cooling. While typical data centres use air cooling, i.e. extra energy is needed to cool down air that is then blown into the racks, the DEEP-EST water cooling allows to use water at normal temperatures and pipe it through those components that create most of the heat. This will warm up the water and the energy contained in this warm water can then be even used for something else. I.e. instead of needing extra energy from cooling, the DEEP-EST warm water cooling allows to even gain energy (of course, this is energy inserted in the system by the electrical power that the supercomputing components consume). You see the water pipes of the newly installed CPU cluster module in the middle rack below:

Talking about energy efficiency: another trend are field-programmable gate arrays (FPGAs) that are more energy efficient than CPUs or GPUs. These are as well used in one of the specialised DEEP-EST modules.

The downside of the usage of accelerators is that they need special programming. University of Iceland is as DEEP-EST member developing machine learning software that exploits the DEEP-EST Modular Supercomputing Architecture (MSA) as good as possible. This includes clustering (DBSCAN) and classification via Support Vector Machines (SVMs) and Deep Learning/Deep Neural Networks.

You can follow the progress this project on the DEEP-EST web site and Twitter channel.

Climate change is real and will affect us all. So it is good that the Fridays for Future protests have reached Iceland. Scientists in German-speaking countries made their statement that these concerns are justified and supported by the best available science: The current measures for climate, biodiversity, forest, marine, and soil protection are far from sufficient.

I am participating in the eSTICC (eScience Tools for Investigating Climate Change at High Northern Latitudes) NordForsk-funded research project. As part of the project an impressing (or depressing) simulation of the Greenland ice sheet and climate change has been created (the simulations ran on a supercomputer located in Iceland) that shows the surface air temperature in the Arctic and Greenland glacier ice thickness, e.g. when will the Arctic sea ice be gone during summer (we got used to already now) and during winter (=no ice at the North pole in winter -- imagine this) according to the simulations:

We all should act:

• A carbon tax would be a strong incentive for all sectors to reduce CO2, so urge politicians to introduce a carbon tax. (The 2018 Nobel Prize in Economic Sciences was about the effects of a carbon tax on climate while still allowing economic growth.)
• No joke: Consume less meat and milk products! What many do not have in mind is the climate impact of animal farming (for meat and dairy): e.g. cows emit a lot of methane (which has 28 times the impact on temperature of a carbon dioxide emission of the same mass).
• And finally: reduce flights! One transatlantic flight (back and forth) is in terms of CO2 comparable to one year of average car usage. While living on a far-away island like Iceland involves flying, at least compensate by CO2 offsetting, e.g. via atmosfair or myclimate.

If you are a member of the HPC community, i.e. have some experience in HPC (either in an HPC expert role at a computing centre or in a user role such as a scientist), please fill out the questionnaire below where we ask for your usage of software development best practises. Filling out the survey just takes 5 minutes:

Software Engineering for High-Performance Computing Survey

If you want to advertise the survey, below is a slide and a flyer:
Slide (PPT)
Flyer (PPT) / Flyer (PDF)

The NordForsk-funded Nordic Center of Excellence (NCoE) eSTICC (eScience Tools for Investigating Climate Change at High Northern Latitudes) is holding a breakout session at the Arctic Circle assembly 2017 in Reykjavik. University of Iceland is a member of eSTICC and this session has been organised and is chaired by Helmut Neukirchen from the Faculty of Industrial Engineering, Mechanical Engineering and Computer Science of the University of Iceland.

This breakout session presents results from top research groups working in the fields of climate research supported by eScience, such as extensive simulations of climate change. The session takes place on Saturday, October 14th at 17:30-19:00 in room Hafnarkot on the first floor of Harpa Conference Centre. The talks can be found on the program of the Arctic Circle website and here.

A consortium including the University of Iceland participated successfully in the European Commission's Horizon 2020 research program call FET PROACTIVE – HIGH PERFORMANCE COMPUTING: Co-design of HPC systems and applications. The University of Iceland's team is lead by Helmut Neukirchen together with Morris Riedel. The secured funding for University of Iceland (387 860 EUR for three years project duration starting from 1st July 2017) will be, among others, used to hire a researcher who will perform ambitious research by providing a scientific parallel applications from the field of machine learning for extreme scale/pre-exascale high-performance computing, i.e. creating next-generation software for the next-generation supercomputing hardware.

More details can be found here.

Update deadline extended until 3. February 2017!

Due to the fast development of information technology, the understanding of phenomena in natural, engineer, economy and social sciences increasingly relies on computer simulations. Simulation-based analysis and engineering techniques are traditionally a research focus of Clausthal University of Technology and University of Göttingen, which is especially reflected in their common interdisciplinary research cluster "Simulation Science Center Clausthal-Göttingen". In this context, the first "Clausthal-Göttingen International Workshop on Simulation Science" aims to bring together researchers and practitioners from both industry and academia to report on the latest advances in simulation science.

The workshop considers the broad area of modeling & simulation with a focus on:

• Simulation and optimization in networks:
  Public & transportation networks, computer & sensor networks, queuing networks, Internet of Things (IoT) environments, simulation of uncertain optimization problems, simulation of complex stochastic systems
• Simulation of materials:
  Development and applications of computational techniques in material and process simulation, simulation at micro (atomistic), meso and macro (continuum) scales including scale bridging, diffusive, convective transport and chemical processes in materials, simulation of granular matter
• Distributed simulations:
  Technology enabler for distributed simulation (e.g., simulation support for vector and parallel computing architectures, grid-based systems and cloud-based systems), methods for distributed simulation (e.g., agent-based simulation, multi-level simulation, and simulation for big data analytics, fusion and mining), application examples (e.g., simulation-based quality assurance and high-energy physics)

27 - 28 April 2017, Göttingen, Germany

Extended Abstract (2-3 pages) Submission: 20 Jan 2017

Workshop web page

Call for Papers: Download

In the age of Big Data and big data frameworks such as Apache Spark, one might be tempted to think that supercomputing/high-performance computing (HPC) is obsolete. But in fact, Big Data processing and HPC are different and one platform cannot replace the other. I outline this in a presentation on Science Day of the University of Iceland's School of Engineering and Natural Sciences Saturday October 29 2016. (Note that there is nowadays some convergence, and a graph-processing benchmark top 500 list to resemble less CPU-intensive workloads in HPC.)

Furthermore, the available open-source implementations of algorithms (e.g. clustering using DBSCAN) are currently much faster in HPC and the available Big Data implementations do in fact not even scale beyond a handful of nodes. Results of a case study performed during my guest research stay at the research group High Productivity Data Processing of the Federated Systems and Data division at the Jülich Supercomputing Centre (JSC) are published in this Technical Report.

One of the reviewers of the 1st IEEE International Workshop on Big Spatial Data (BSD 2016) seems not to like the message that Big Data needs to do its homework to match HPC, hence my paper was rejected. While I assume that an HPC conference (such as ISC) might accept it, it would be nice to get the message to the Big Data community: I might submit it to The 6th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics or later at BDCloud 2017 : The 7th IEEE International Conference on Big Data and Cloud Computing. Non-public source implementations may also be worth considering: A novel scalable DBSCAN algorithm with Spark or A Parallel DBSCAN Algorithm Based on Spark. (If we get access to the implementation, but lacking possibility of reproducing/verifying scientific results is another story covered in my Technical Report.) Also, I might add threats to validity (such as construct, internal and external validity [Carver, J., VanVoorhis, J., Basili, V., August 2004. Understanding the impact of assumptions on experimental validity.])

Update from 9.11.2016: Erich Schubert (thanks!) pointed me to this related article "The (black) art of runtime evaluation: Are we comparing algorithms or implementations?" (DOI: 10.1007/s10115-016-1004-2) which support my findings. A statement from that article on k-means: "Judging from the measured runtime and even assuming zero network overhead, we must assume that a C++ implementation using all cores of a single modern PC will outperform a 100-node cluster easily." For DBSCAN, they show that a C++ implementation is one order of magnitude faster than the Java ELKI (which confirms my measurements concerning the C++ HPDBSCAN and the Java ELKI) on their used dataset. They also support my claim that the implementation matters: "Good implementations with index accelerations would process this data set in less than 2 seconds, whereas the fastest linear scan implementation took over 90 seconds, and naïve implementations would frequently require over 100 times the best runtime. But not every index we evaluated was implemented correctly, and sometimes an index was even slower than the linear scan. Between different implementations using linear scan, we can observe runtime differences of more than two orders of magnitude. Comparing the fastest implementation measured (optimized C++ code for this task) to the slowest implementation (outdated versions of Weka), we observe four orders of magnitude: less than two seconds instead of over four hours."