Related papers: Tarema: Adaptive Resource Allocation for Scalable …
We present a number of novel algorithms, based on mathematical optimization formulations, in order to solve a homogeneous multiprocessor scheduling problem, while minimizing the total energy consumption. In particular, for a system with a…
High Speed computing meets ever increasing real-time computational demands through the leveraging of flexibility and parallelism. The flexibility is achieved when computing platform designed with heterogeneous resources to support…
For the last decade, layered stacks have dominated the way of reasoning about architectures for quantum networks. However, layered architectures impose stringent design and timing constraints on quantum networks, adding additional latency…
The convergence of IoT, Edge, Cloud, and HPC technologies creates a compute continuum that merges cloud scalability and flexibility with HPC's computational power and specialized optimizations. However, integrating cloud and HPC resources…
Traditional heterogeneous parallel algorithms, designed for heterogeneous clusters of workstations, are based on the assumption that the absolute speed of the processors does not depend on the size of the computational task. This assumption…
Scheduling of constrained deadline sporadic task systems on multiprocessor platforms is an area which has received much attention in the recent past. It is widely believed that finding an optimal scheduler is hard, and therefore most…
Using correct design metrics and understanding the limitations of the underlying technology is critical to developing effective scheduling algorithms. Unfortunately, existing scheduling techniques used \emph{incorrect} metrics and had…
With the rise of Visual and Language Pretraining (VLP), an increasing number of downstream tasks are adopting the paradigm of pretraining followed by fine-tuning. Although this paradigm has demonstrated potential in various multimodal…
Data analytics systems commonly utilize in-memory query processing techniques to achieve better throughput and lower latency. Modern computers increasingly rely on Non-Uniform Memory Access (NUMA) architectures in order to achieve…
To deliver high performance in power limited systems, architects have turned to using heterogeneous systems, either CPU+GPU or mixed CPU-hardware systems. However, in systems with different processor types and task affinities, scheduling…
Many real-world scientific workflows can be represented by a Directed Acyclic Graph (DAG), where each node represents a task and a directed edge signifies a dependency between two tasks. Due to the increasing computational resource…
Warehouse-scale cloud datacenters co-locate workloads with different and often complementary characteristics for improved resource utilization. To better understand the challenges in managing such intricate, heterogeneous workloads while…
Computer-based scientific experiments are becoming increasingly data-intensive, necessitating the use of High-Performance Computing (HPC) clusters to handle large scientific workflows. These workflows result in complex data and control…
Systems for processing big data---e.g., Hadoop, Spark, and massively parallel databases---need to run workloads on behalf of multiple tenants simultaneously. The abundant disk-based storage in these systems is usually complemented by a…
Heterogeneous scientific workflows consist of numerous types of tasks that require executing on heterogeneous resources. Asynchronous execution of those tasks is crucial to improve resource utilization, task throughput and reduce workflows'…
Containers are standalone, self-contained units that package software and its dependencies together. They offer lightweight performance isolation, fast and flexible deployment, and fine-grained resource sharing. They have gained popularity…
Energy consumption is one of the most critical concerns in designing computing devices, ranging from portable embedded systems to computer cluster systems. Furthermore, in the past decade, cluster systems have increasingly risen as popular…
There are many science applications that require scalable task-level parallelism and support for flexible execution and coupling of ensembles of simulations. Most high-performance system software and middleware, however, are designed to…
The growing disparity between CPU core counts and available memory bandwidth has intensified memory contention in servers. This particularly affects highly parallelizable applications, which must achieve efficient cache utilization to…
The increasing availability of cloud computing services for science has changed the way scientific code can be developed, deployed, and run. Many modern scientific workflows are capable of running on cloud computing resources. Consequently,…