Object-oriented programming languages provide a rich set of features that provide significant software engineering benefits. The increased productivity provided by these features comes at a justifiable cost in a more sophisticated runtime system whose responsibility is to implement these features efficiently. However, the virtualization introduced by this sophistication provides a significant challenge to understanding complete system performance, not found in traditionally compiled languages, such as C or C++. Thus, understanding system performance of such a system requires profiling that spans all levels of the execution stack, such as the hardware, operating system, virtual machine, and application. In this work, we suggest an approach, called vertical profiling, that enables this level of understanding. We illustrate the efficacy of this approach by providing deep understandings of performance problems of Java applications run on a VM with vertical profiling support. By incorporating vertical profiling into a programming environment, the programmer will be able to understand how their program interacts with the underlying abstraction levels, such as application server, VM, operating system, and hardware.

/pdf/vertical-profiling-understanding-the-behavior-of-object-12ksghoeo1.pdf

Vertical profiling: understanding the behavior of object-priented applications

Datacenter disaggregation provides numerous benefits to both the datacenter operator and the application designer. However switching from the server-centric model to a disaggregated model requires developing new programming abstractions that can achieve high performance while benefiting from the greater elasticity. To explore the limits of datacenter disaggregation, we study an application area that near-maximally benefits from current server-centric datacenters: dense linear algebra. We build NumPyWren, a system for linear algebra built on a disaggregated serverless programming model, and LAmbdaPACK, a companion domain-specific language designed for serverless execution of highly parallel linear algebra algorithms. We show that, for a number of linear algebra algorithms such as matrix multiply, singular value decomposition, Cholesky decomposition, and QR decomposition, NumPyWren's performance (completion time) is within a factor of 2 of optimized server-centric MPI implementations, and has up to 15% greater compute efficiency (total CPU-hours), while providing fault tolerance.

https://dl.acm.org/doi/pdf/10.1145/3419111.3421287

Serverless linear algebra

Cloud computing is a new and rapidly emerging computing paradigm where applications, data and IT services are provided over the Internet. The task-resource management is the key role in cloud computing systems. Task-resource scheduling problems are premier which relate to the efficiency of the whole cloud computing facilities. Task-resource scheduling problem is NP-complete. In this paper, we consider an approach to solve this problem optimally. This approach is based on constructing a logical model for the problem. Using this model, we can apply algorithms for the satisfiability problem (SAT) to solve the task-resource scheduling problem. Also, this model allows us to create a testbed for particle swarm optimization algorithms for scheduling workflows.

Task-resource scheduling problem

Multi-core processors are increasingly being considered as a means to provide the performance required by future safety-critical embedded systems. In this line, Aeroflex Gaisler has developed, in conjunction with the European Space Agency, the NGMP, a quad-core processor to be used in the future space missions of the Agency. Unfortunately, the use of multi-core processors in industrial domains is not straightforward since it poses various challenges on the timing behavior of the system. This is mainly due to the interferences tasks suffer when accessing hardware shared resources and which can affect their WCET. Although the effect of inter-task interferences in multi-core shared resources on real-time applications has received attention from academia, most of the solutions proposed require hardware changes. The lack of quantitative studies of the slowdown on applications' performance caused by inter-task interferences on real COTS multi-core processors, limit their use by industry.As a first step to understand the effect of inter-task interference in real COTS processors, this paper evaluates the timing predictability properties of the NGMP. In particular, we measure the maximum variation on tasks' execution time due to inter-task interferences accessing NGMP's shared hardware resources. To that end, we use a set of specialized micro-benchmarks designed to stress specific processor shared resources. The results of this can be useful for developing interference-aware WCET estimation methodologies and scheduling algorithms for real-time applications running on embedded multi-core processors.

/pdf/assessing-the-suitability-of-the-ngmp-multi-core-processor-1825cp5232.pdf

Assessing the suitability of the NGMP multi-core processor in the space domain

Last year at OOPSLA we presented a methodology, vertical profiling, for understanding the performance of object-oriented programs. The key insight behind this methodology is that modern programs run on top of many layers (virtual machine, middleware, etc) and thus we need to collect and combine information from all layers in order to understand system performance. Although our methodology was able to explain previously unexplained performance phenomena, it was extremely labor intensive. In this paper we describe and evaluate techniques for automating two significant activities of vertical profiling: trace alignment and correlation. Trace alignment aligns traces obtained from separate runs so that one can reason across the traces. We are not aware of any prior approach that effectively and automatically aligns traces. Correlation sifts through hundreds of metrics to find ones that have a bearing on a performance anomaly of interest. In prior work we found that statistical correlation was only sometimes effective. We have identified highly-effective approaches for both activities.For aligning traces we explore dynamic time warping, and for correlation we explore eight correlators based on statistical correlation, distance measures, and piecewise linear segmentation. Although we explore these activities in the context of vertical profiling, both activities are widely applicable in the performance analysis area.

Automating vertical profiling

This 15th Annual SC conference brings together a worldwide community to celebrate its achievements and envision the future of high performance computing, networking, and data technologies. Our theme, "From Terabytes to Insights," emphasizes the opportunity to use technology to transform an ever-increasing flow of data into the knowledge that will impact education, science, business, government, and every aspect of society.The technical program features an outstanding set of speakers - in plenary, masterworks, and papers -- who address key technical, policy, and real-world application issues of HPC. Tutorials and the Education program provide unparalleled opportunities for learning at and beyond the conference. Panel discussions and Birds of a Feather sessions are opportunities for community interaction and discussion. The Awards program recognizes the highest achievements in our field and at the conference.We feature a record number of exhibits from the world's top technology companies, cutting-edge start-ups, research centers, government labs, and top universities, providing an incomparable view of our country's accomplishments. Posters are integrated into the new 'Academic Village' section of the exhibition. And SCinet 2002, in place for the duration of the conference, is one of the most powerful networks on earth.The IntelliBadge™ demonstration project will link 'smart' sensor technology to on-the-floor computing and visualization resources as a service to attendees. Technical program attendees who participate in IntelliBadge will be able to pinpoint events that meet their needs and interests, locate people with similar interests, and even find restaurants to suit their tastes.We invite you to enjoy colorful, historic Baltimore, a city of great writers (Edgar Allen Poe, H. L. Mencken), great musicians (Eubie Blake, Billie Holiday), and the home of Fort McHenry, where Francis Scott Key penned "The Star Spangled Banner." The city's Inner Harbor is a perfect example of a rejuvenated inner city neighborhood. Conference hotels, restaurants, and entertainment facilities such as Harborplace, the National Aquarium, and the Maryland Science Center are an easy walk from the convention center.So, whether you want to build the world's most powerful supercomputer, develop lasting scientific collaborations, or simply glimpse the future technologies that will shape our world, you have come to the right place - SC2002 in Baltimore!

Proceedings of the 2002 ACM/IEEE conference on Supercomputing

An examination of how the technology landscape has changed and possible future directions for HPC operations and innovation.

HPC Forecast

The world of computing is in rapid transition, driven by the growth of smartphones, cloud services, and embedded devices, all while the future of semiconductors is in great flux due to the slowing of Moore’s Law and increasing semiconductor foundry costs. Concomitantly, the future of advanced scientific computing (aka supercomputing or high-performance computing (HPC)), is at an important inflection point. For the last 60 years, the world’s fastest computers were almost exclusively produced in the United States on behalf of scientific research in the national laboratories. Change is now in the wind. While costs now stretch the limits of U.S. government funding for advanced computing, Japan and China are now leaders in the bespoke HPC systems funded by government mandates. However, another, perhaps even deeper, fundamental change has occurred. The major cloud vendors have invested in global networks of massive scale systems that dwarf today’s HPC systems. Driven by the computing demands of AI, these cloud systems are increasingly built using custom semiconductors, reducing the financial leverage of traditional computing vendors, while also reshaping we think about the nature of scientific computation. Building the next generation of leading edge HPC systems will require rethinking many fundamentals and historical approaches by embracing end-to-end co-design; custom hardware configurations and packaging; large-scale prototyping, as was common thirty years ago; and collaborative partnerships with the dominant computing ecosystem companies. Universities, industry and governments ll need to reinvest in the basics of collaborative co-design, chiplet systems, quantum technology and new fabrication technologies. We need to reinvest in training the next generation of computer architects and collaborate on experimental prototypes.

HPC Forecast: Cloudy and Uncertain

/pdf/committee-on-science-space-and-technology-subcommittee-on-2zrg5pary3.pdf

Committee on science, space, and technology subcommittee on energy

Pah contamination in town creek, nc from a historical seep

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