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Parallel processing is considered as todays and future trend for improving performance of computers. Computing devices ranging from small embedded systems to big clusters of computers rely on parallelizing applications to reduce execution…
Multicore CPU architectures have been established as a structure for general-purpose systems for high-performance processing of applications. Recent multicore CPU has evolved as a system architecture based on non-uniform memory…
Pattern matching on large graphs is the foundation for a variety of application domains. Strict latency requirements and continuously increasing graph sizes demand the usage of highly parallel in-memory graph processing engines that need to…
Disaggregated systems have a novel architecture motivated by the requirements of resource intensive applications such as social networking, search, and in-memory databases. The total amount of resources such as memory and CPU cores is very…
Multicore systems present on-board memory hierarchies and communication networks that influence performance when executing shared memory parallel codes. Characterising this influence is complex, and understanding the effect of particular…
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…
Emerging computing architectures such as near-memory computing (NMC) promise improved performance for applications by reducing the data movement between CPU and memory. However, detecting such applications is not a trivial task. In this…
Non-Uniform Memory Access (NUMA) architecture imposes numerous performance challenges to today's cloud workloads. Due to the complexity and the massive scale of modern warehouse-scale computers (WSCs), a lot of efforts need to be done to…
Parallel applications are extremely challenging to achieve the optimal performance on the NUMA architecture, which necessitates the assistance of profiling tools. However, existing NUMA-profiling tools share some similar shortcomings, such…
Requests arriving at main memory are often different from what programmers can observe or estimate by using CPU-based monitoring. Hardware cache prefetching, memory request scheduling and interleaving cause a loss of observability that…
Increasing workload demands and emerging technologies necessitate the use of various memory and storage tiers in computing systems. This paper presents results from a CXL-based Experimental Memory Request Logger that reveals precise memory…
Current and future applications demand ultra-low latency and consistent throughput, yet frequently traverse 5G cellular networks, so cope with volatile packet dynamics, as 5G base station schedulers dynamically react to user workloads and…
There is a great diversity of clustering and community detection algorithms, which are key components of many data analysis and exploration systems. To the best of our knowledge, however, there does not exist yet any uniform benchmarking…
Motivated by the need for adaptive, secure and responsive scheduling in a great range of computing applications, including human-centered and time-critical applications, this paper proposes a scheduling framework that seamlessly adds…
Near-memory Computing (NMC) promises improved performance for the applications that can exploit the features of emerging memory technologies such as 3D-stacked memory. However, it is not trivial to find such applications and specialized…
The distributed shared memory (DSM) architecture is widely used in today's computer design to mitigate the ever-widening processing-memory gap, and inevitably exhibits non-uniform memory access (NUMA) to shared-memory parallel applications.…
With the surge of inexpensive computational and memory resources, neural networks (NNs) have experienced an unprecedented growth in architectural and computational complexity. Introducing NNs to resource-constrained devices enables…
Compute-Near-Memory (CNM) systems offer a promising approach to mitigate the von Neumann bottleneck by bringing computational units closer to data. However, optimizing for these architectures remains challenging due to their unique hardware…
Data-intensive scientific workflows increasingly rely on high-performance computing (HPC) systems, complementing traditional Grid and Cloud platforms. However, workflow scheduling on HPC infrastructures remains challenging due to the…
Computers used for data analytics are often NUMA systems with multiple sockets per machine, multiple cores per socket, and multiple thread contexts per core. To get the peak performance out of these machines requires the correct number of…