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Oct. 13, 2021 ACM, the Association for Computing Machinery and IEEE Computer Society have named David Abramson, a Professor at the University of Queensland, as the recipient of the 2021 ACM-IEEE CS Ken Kennedy Award.Abramson is recognized for contributions to parallel and distributed computing tools, with application from quantum chemistry to engineering design. He is also cited for his mentorship and service to the field.

Technical Contributions

Abramson has performed pioneering research in the design, implementation, and application of software tools for parallel and distributed systems. He has conducted foundational research in distributed and parallel middleware, addressing programmer productivity and software correctness, and has influenced multiple generations of researchers. His papers have been cited more than 12,000 times.

Two highly-regarded tools developed by Abramson include Nimrod, a family of software systems that support the execution of distributed parameter sweeps, searches, and workflows; and Guard, a performance tuning and debugging tool.

The Nimrod template is common in many fields and is well suited to execution in distributed environments. Nimrod makes it possible to write concisely complex parameter sweeps which entail executing an algorithm repeatedly with varying parametersand supports advanced searches that integrate optimization algorithms, design of experiments methods, and scientific workflows. Additionally, the Nimrod project spawned a family of tools that make it easy to specify complex computational experiments and has resulted in a spinoff commercial product called EnFuzion, which has been widely adopted for power grid and simulation.

Abramson designed Guard with a hybrid debugging scheme that tests new versions of a program against reference versions known to be correct. Guard greatly enhances programmers ability to locate and fix errors in new software versions. The technology was licensed to Cray Inc. (now HPE) and is distributed on Cray supercomputers. As a result, it has been deployed at major international supercomputing centers, including the US National Energy Research Scientific Computing Center (NERSC) and the Swiss National Supercomputing Centre (CSCS).

Mentorship

Abramsonhas been an advisor to two dozen graduate students in computer science, as well as countless undergraduate and high school students.

Among his most important initiatives, Abramson has been an international driver of the PRIME initiative, an NSF-funded University of California San Diego program that enables undergraduate students to take research internships abroad. Inspired by the success of PRIME, he has introduced similar programs for Australian undergraduates to travel abroad for internships, and he has organized travels for Australian students to top research centers in the US and the UK annually for over 12 years. Since 2011, he has run a unique program that supports Australian high school students attending SC, the leading high performance computing conference.

Also in the mentoring arena, Abramson started streaming video HPC seminars that have allowed Australian students to engage with world leaders, and he launched the Early Adopters PhD Workshop at SC09. Distinct from other doctoral showcases, the workshop specifically targets research students from fields outside of computer science who are applying HPC tools in their research.

Service to the Field

Over his career, Abramson has been General Chair, Program Committee Chair, or program committee member of many conferences related to performance and programmer productivity (on average about eight per year), including IPDPS, HiPC, HPC Asia, HPDC, ICPADS, Cluster, SC, CCGrid, Grid, and e-Science.

He is currently the Chair of the e-Science Steering Committee and has served in several senior roles in the IEEE/ACM SC series, including Chair of the Technical Papers Committee (2021) and Test of Time Award Committee (2018), Co-chair of the Invited Speakers Committee (2019), and More than HPC Plenary Committee (2020).

ACM and IEEE CS co-sponsor the Kennedy Award, which was established in 2009 to recognize substantial contributions to programmability and productivity in computing and significant community service or mentoring contributions. It was named for the late Ken Kennedy, founder of Rice Universitys computer science program and a world expert on high performance computing.The Kennedy Award carries a US $5,000 honorarium endowed by IEEE CS and ACM. The award will be formally presented to Abramson in November atThe International Conference for High Performance Computing, Networking, Storage and Analysis (SC21).

About ACM

ACM, the Association for Computing Machinery, is the worlds largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the fields challenges. ACM strengthens the computing professions collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.

Source: Association for Computing Machinery

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University of Queensland Professor David Abramson Wins High Performance Computing Award - HPCwire

Neural networks can learn to solve all sorts of problems, from identifying cats in photographs to steering a self-driving car. But whether these powerful, pattern-recognizing algorithms actually understand the tasks they are performing remains an open question.

For example, a neural network tasked with keeping a self-driving car in its lane might learn to do so by watching the bushes at the side of the road, rather than learning to detect the lanes and focus on the roads horizon.

Researchers at MIT have now shown that a certain type of neural network is able to learn the true cause-and-effect structure of the navigation task it is being trained to perform. Because these networks can understand the task directly from visual data, they should be more effective than other neural networks when navigating in a complex environment, like a location with dense trees or rapidly changing weather conditions.

In the future, this work could improve the reliability and trustworthiness of machine learning agents that are performing high-stakes tasks, like driving an autonomous vehicle on a busy highway.

Because these brain-inspired machine-learning systems are able to perform reasoning in a causal way, we can know and point out how they function and make decisions. This is essential for safety-critical applications, says co-lead author Ramin Hasani, a postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Co-authors include electrical engineering and computer science graduate student and co-lead author Charles Vorbach; CSAIL PhD student Alexander Amini; Institute of Science and Technology Austria graduate student Mathias Lechner; and senior author Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and director of CSAIL. The research will be presented at the 2021 Conference on Neural Information Processing Systems (NeurIPS) in December.

An attention-grabbing result

Neural networks are a method for doing machine learning in which the computer learns to complete a task through trial-and-error by analyzing many training examples. And liquid neural networks change their underlying equations to continuously adapt to new inputs.

The new research draws on previous work in which Hasani and others showed how a brain-inspired type of deep learning system called a Neural Circuit Policy (NCP), built by liquid neural network cells, is able to autonomously control a self-driving vehicle, with a network of only 19 control neurons.

The researchers observed that the NCPs performing a lane-keeping task kept their attention on the roads horizon and borders when making a driving decision, the same way a human would (or should) while driving a car. Other neural networks they studied didnt always focus on the road.

That was a cool observation, but we didnt quantify it. So, we wanted to find the mathematical principles of why and how these networks are able to capture the true causation of the data, he says.

They found that, when an NCP is being trained to complete a task, the network learns to interact with the environment and account for interventions. In essence, the network recognizes if its output is being changed by a certain intervention, and then relates the cause and effect together.

During training, the network is run forward to generate an output, and then backward to correct for errors. The researchers observed that NCPs relate cause-and-effect during forward-mode and backward-mode, which enables the network to place very focused attention on the true causal structure of a task.

Hasani and his colleagues didnt need to impose any additional constraints on the system or perform any special set up for the NCP to learn this causality it emerged automatically during training.

Weathering environmental changes

They tested NCPs through a series of simulations in which autonomous drones performed navigation tasks. Each drone used inputs from a single camera to navigate.

The drones were tasked with traveling to a target object, chasing a moving target, or following a series of markers in varied environments, including a redwood forest and a neighborhood. They also traveled under different weather conditions, like clear skies, heavy rain, and fog.

The researchers found that the NCPs performed as well as the other networks on simpler tasks in good weather, but outperformed them all on the more challenging tasks, such as chasing a moving object through a rainstorm.

We observed that NCPs are the only network that pay attention to the object of interest in different environments while completing the navigation task, wherever you test it, and in different lighting or environmental conditions. This is the only system that can do this casually and actually learn the behavior we intend the system to learn, he says.

Their results show that the use of NCPs could also enable autonomous drones to navigate successfully in environments with changing conditions, like a sunny landscape that suddenly becomes foggy.

Once the system learns what it is actually supposed to do, it can perform well in novel scenarios and environmental conditions it has never experienced. This is a big challenge of current machine learning systems that are not causal. We believe these results are very exciting, as they show how causality can emerge from the choice of a neural network, he says.

In the future, the researchers want to explore the use of NCPs to build larger systems. Putting thousands or millions of networks together could enable them to tackle even more complicated tasks.

This research was supported by the United States Air Force Research Laboratory, the United States Air Force Artificial Intelligence Accelerator, and the Boeing Company.

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These neural networks know what they're doing - MIT News

When he died in 1827 aged 56, Ludwig van Beethoven left his 10th symphony unfinished. Only a few handwritten notes briefly detailing his plans for the piece have survived, with most just being incomplete ideas or fragments of themes or melodies.

Now, a multidisciplinary team of computer scientists at Rutgers University-based start-up Playform AI have trained an artificial intelligence to mimic the great composers style and used it to write a complete symphony based on these initial sketches.

We spoke to the lead researcher on the project, Professor Ahmed Elgammal, to find out more.

Beethoven left sketches in different forms, mainly musical sketches, but also some written notes with some ideas in as well. Previously, in 1988 [English musicologist] Barry Cooper used the majority of these sketches, about 250 bars of music, that were meant for a first movement [in his attempt to complete the symphony].

But what was left behind is really very little. So basically, like three bars of music here and four bars of music there and some rough sketches, which sound like basically the starting points of the main themes in the movements that he [Beethoven] wanted to write.

When you look at Beethoven and other classical composers, thats usually the case. I mean, usually they work with a main theme and develop it into a sequence of a couple of minutes and then another theme comes. Thats the traditional way of composing, and thats exactly what the AI needed to learn how Beethoven and other classical composers start with a theme and develop it. Like in the Fifth Symphony da da da dah. And then take that and evolve a whole movement around it.

Read more about artificial intelligence and music:

The way AI generates music in general is very similar to the way your email, for example, tries to predict the next word for you. So, when you write an email, you find it jumps into suggesting what you might want to write next.

Its the same concept, basically the AI has to learn from a lot of musical data. It asks what would be the next note given what you just wrote? And if you can predict the next note, then you can predict the next note and the next note and so on. Thats the main concept.

But what we soon realise is that if you start picking up the suggestions from the phone for next word and start writing just based on the AIs suggestions, it doesnt really hold for a long time. And thats what happens with music. If you just give it a starting point and leave it to predict, yes, it can predict a couple of notes. But then after that, it becomes nonsense more or less, and is no longer faithful to the main theme.

So that was the main challenge. How can we let the AI stick to the main theme and develop it? So this is where the role of the human expert working with the AI comes in. So we had to work with human experts to annotate and label a lot of music for us to tell the AI what the theme was and where the development of the theme was in a lot of pieces of music. So basically, the AI learnt as a student. That made a big difference because then the AI could really keep sticking to the theme.

Also, the AI had to compose the music in a specific musical form. So if you are composing for a scherzo movement or a trio part of the movement or a fugue etc, each of these musical forms have certain specific structure. The AI also had to learn how to write a fugue, how to write a trio, how to write a fugue, and how to write a scherzo.

It was very challenging because Beethoven only wrote nine symphonies. Thats a very small dataset compared to the scale of what the AI needed to do. So, the way we approached this was to first imagine ourselves like a young Beethoven learning about music. What he would have listened to?

So, we trained our first version of the AI as if it was somebody living in the 18th Century listening to baroque music like Bach, as well as Hayden and Mozart. And so that was the first version of the AI, which basically would be the kind of music anyone living in that era would study to compose. And then we took that and trained it specifically on Beethoven on old Beethoven sonatas, concertos, string quartets and the symphonies as well, so not only symphonies.

We first trained the AI to generate the composition as two lines of music, not as a full symphony, which is a typical way of a composer works by just composing first and then orchestrating. So then, we had another AI that would take that composition and learn how to orchestrate it. I believe this is very similar to the way humans learn you cannot really master fourth-level college without going through the first and second and third levels first. Its always incremental.

The symphony was premiered by The Beethoven Orchestra Bonn on 9 October 2021 Deutsche Telekom

The way we harmonise music is very similar to how we use AI to translate languages. Like when you use Google Translate or another AI to translate a sentence from one language to another. These kind of models used in translation learn a lot of background sentences. So, what is the sentence in German? What is the sentence in English? And from that, they try to learn how to translate them.

So basically, imagine you have these models [for harmonisation]. You put the melody in one side and on the other side you put in how Beethoven would harmonise it so the AI learns how to translate a melody line into harmonised music.

The thing about music is that its very structured and follows a lot of rules. But this is very hard for us to capture and write down. You really have to have a PhD in musicology with a speciality in Beethoven to really understand that. But the machine is able to capture that statistically and mathematically in a very implicit way and be able to use that to give us this harmonisation.

You got it right. That decision is just an extension of the harmonisation. We wanted the machine to translate the composition into multi-track instrumentation, which we also did by training the AI based on how Beethoven and other composers would do so.

Their response is really mixed. There are people who loved this very much, and love the idea of having an AI that understands music and can help you finish your composition or have you explore different musical ideas.

But on the other side of the spectrum, there are people who just reject even the concept of being able to complete a Beethoven symphony using AI. They are afraid of AI taking their jobs and think that it has nothing to do with this kind of thing.

Yeah. I have no doubt about that, we did that in visual art a couple of years ago where we developed an almost autonomous AI artist we had look at, lets say, the last 500 years of western art. The task was basically to generate new artworks that didnt follow any existing style.

If the AI generated an impressionist or a Picasso kind of art or a Renaissance-style artwork, it could realise and so it would have to learn how to create something new.

The challenge with this project was actually the constraints the fact that the AI was not generating music by itself but generating music that is based on Beethovens genius and also following the sketches. This makes it even more difficult. The high bar, of course, of expectation was due to the sketches coming from Beethoven. But when it comes to generating music autonomously I think thats an easier task.

Listen to the symphony below:

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How an AI finished Beethoven's last symphony and what that means for the future of music - BBC Focus Magazine

Artificial Intelligence has been a key talking point in the high-performance computing and computer science communities for decades now.

First it was a thought experiment and theoretical discussion point, but in recent years, it has become a practical area of focus for many scientists, researchers, and engineers, as well as businesses, universities, and government agencies. As the shift from theory to practice accelerated, so too did the excitement and scope of the expected impact of this technology on our society.

Autonomous vehicles, just-in-time maintenance, and real-time image processing are just a few of the ways you can apply AI at the edge. These and many other use cases are considered realistic deployments of complex deep learning training and inference models.

Until recently, these technologies have relied on large datacenters and inefficient algorithms to build, train, and run these models. For example, a decade ago, IBMs Watson became famous as one of the first modern AI systems. It required ninety 4U servers to generate 80 TFLOPs of performance. But today, you can beat that performance with just a handful of white box GPU-accelerated servers.

This growth in system efficiency and performance has created an environment in the datacenter where machine learning and deep learning models can thrive. Simultaneously, the barrier to entry into AI workflows is being lowered, opening the doors to new use cases, algorithms, and benefits from AI.

Still, we havent seen the world-altering effects of AI outside of digital experiences like social media and search engines. What is keeping more tangible use cases like autonomous vehicles from wide-scale adoption? Challenges in edge computing.

Historically, datacenter environments have had the distinct advantage of having large amounts of equipment, power, cooling, physical space, data storage, and high-performance networking available to provide ever-increasing levels of performance.

Trying to create similar performance at the edge has been a key challenge because engineers lose the luxuries of large, power-hungry, and loud clusters. Edge devices have environmental, operational, and practical limitations that are not present in the controlled environment of a datacenter.

A well-trained and efficient AI model allows lower-power edge devices to handle inference tasks, but they are unable to also train and improve their own algorithms. Instead, workflows must rely on communication between edge devices and the datacenter.

Machine learning and deep learning rely on huge volumes of data that must be stored and processed. To use these technologies at the edge requires a tiered processing system. This allows data to be uploaded to the datacenter for processing, where algorithms can be further refined and downloaded to the edge device. That device, in turn, becomes better able to act or output in a desired fashion, without having to wait for the datacenter to execute a decision. This positive feedback loop is key to building effective and powerful AI applications that can perform on the edge.

Multiple forces are converging to allow this model to operate practically.

Processing power for AI has always been a limiting factor. Now, however, thanks to GPU acceleration and great advances in CPU performance and efficiency, that has changed. The state-of-the-art has moved so much that traditional chip manufacturers are creating processors that are much faster and more efficient than chips from just a few years ago, making them well-suited for edge applications. Some manufacturers are going so far as to make -specific processors for AI on the edge.

Because of these new capabilities, the overall cost of performance has come down substantially in recent years. This is a critical issue for organizations looking to deploy AI on the edge, since the sheer number of potential edge devices that can run AI far exceeds the number of datacenters.

Another area where historical attempts at AI on the edge have fallen short is in the speed and reliability of communication between the datacenter and the edge. For instance, an autonomous vehicle is responsible for the safety of passengers, pedestrians, and other vehicles. It cannot depend on a slow or unreliable network to operate properly. Fortunately, new 5G wireless, wide-area networks will enable workloads that will drive demand for AI and Compute at the edge.

Just because something is possible does not mean it is practical. Organizations looking to implement AI at the edge often face an uphill battle. Without the proper expertise, developing optimized datacenter systems for machine learning training and designing the necessary edge equipment for inference can be unrealistic for most teams.

Ensuring you have a properly balanced, high-ROI cluster for your AI workload is difficult. But the problem becomes even more challenging when planning for the large amounts of data coming in from hundreds or thousands of edge devices.

Then, if youre able to build that system efficiently, you still need to solve for a completely different technological problem: edge device design. Commercial, off-the-shelf hardware is rarely capable of providing the optimal solution for specific workload needs, let alone the additional environmental and form factor challenges edge clusters face.

For example, what COTS system can operate in extreme temperature or weather conditions? What about safety and security aspects like operating temperatures or data encryption? If you find you need to design a custom device, how do you ensure it has everything you need while meeting regulatory requirements?

These are important things to consider as you plan for an edge AI or inference project. Silicon Mechanics and our partner Comark have many decades of experience in datacenter and ruggedized edge solution design experience between them. Together, weve put together a consideration guide on preparing for 5G edge computing that covers many of the key decisions organizations must make when deploying AI on the edge.

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It's Time for Real-World AI on the Edge - HPCwire

ODonnell was born near Dallas in 1924. He received a bachelors degree in mathematics from The University of the South in Sewanee, Tennessee, followed by an MBA from the Wharton School of the University of Pennsylvania.

Upon graduation, his early path led him into the world of commerce, where he found financial success in the securities industry.

ODonnells formidable intellect and business acumen led him naturally into politics, where he is credited with reshaping the Texas Republican Party during his tenure as the state partys chairman from 1962 to 1969.

During his early 30s, he and Edith founded the ODonnell Foundation, a philanthropic organization focused on higher education support in Texas, which grew to become the fifth largest independent Texan foundation. Over the years they contributed hundreds of millions of dollars to public and private educational institutions, mostly anonymously.

The O'Donnell Foundation focused on four areas: math; science and engineering education; medicine, arts and music education; and K-12 education.

Edith ODonnell was a graduate in psychology from UT Austin. While the two made their impact felt across higher education in Texas, they had special connection to the Forty Acres.

The O'Donnell Foundation and ODonnell family are among the largest donors to UT Austin. Their support included a challenge grant that created 32 one million-dollar chairs in science and engineering at UT Austin. In 2013, the university announced the naming of the O'Donnell Building for Applied Computational Engineering and Sciences, in recognition of their support throughout the years.

The Oden Institute and TACC

Peter ODonnell enjoyed a long friendship with J. Tinsley Oden, professor of mathematics, computer science, mechanical engineering and aerospace engineering and engineering mechanics at UT Austin, and widely considered to be the father of computational mechanics.

ODonnell realized how important high performance computing (HPC) and computational science were to become in higher education, and in Oden he found a partner with the kind of specialized expertise in mathematics, science and engineering to help put both on the map at UT Austin.

O'Donnell and Oden worked closely to create the Institute for Computational Engineering and Sciences in 2002, now called the Oden Institute for Computational Engineering & Sciences and recognized as one of the top computational research institutes in the world.

Thanks to the unwavering generosity of the ODonnells, Oden was able to recruit some of the most talented computational scientists in the field and build a team that could not only expand the mathematical agility of computational science and engineering as a discipline, but also grow the number of potential real-world applications.

O'Donnell also helped UT Austin get ahead in HPC. In 1985, he encouraged university leadership to acquire the powerful CRAY X-MP system, making UT Austin the first university in Texas to have a supercomputer. This led to the establishment in 2001 of a dedicated advanced computing entity, the Texas Advanced Computing Center (TACC).

Encouragement was followed by the foundations generous support of TACC, which now operates two of the most powerful university supercomputers in the world: Frontera and Stampede2. O'Donnell noted, "That's leverage. High performance computing is changing everything."

ODonnell renewed his commitment to TACC in 2012, and to the advancment of data-driven science by supporting the acquisition of high-performance data analysis and storage systems efforts that helped UT Austin become a leader in data science, machine learning and artificial intelligence. The foundation also contributed to the construction of the Advanced Computing Building that houses the bulk of TACCs staff.

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University of Texas Mourns Passing of Peter J. O'Donnell, Jr. - UT News | The University of Texas at Austin