Related papers: Mao: Machine learning approach for NUMA optimizati…
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…
The rise of disaggregated AI GPUs has exposed a critical bottleneck in large-scale attention workloads: non-uniform memory access (NUMA). As multi-chiplet designs become the norm for scaling compute capabilities, memory latency and…
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…
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…
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…
Leadership supercomputers feature a diversity of storage, from node-local persistent memory and NVMe SSDs to network-interconnected flash memory and HDD. Memory mapping files on different tiers of storage provides a uniform interface in…
By exploiting the superiority of non-orthogonal multiple access (NOMA), NOMA-aided mobile edge computing (MEC) can provide scalable and low-latency computing services for the Internet of Things. However, given the prevalent stochasticity of…
Page placement is a critical problem for memoryintensive applications running on a shared-memory multiprocessor with a non-uniform memory access (NUMA) architecture. State-of-the-art page placement mechanisms interleave pages evenly across…
Processing-using-DRAM (PUD) architectures impose a restrictive data layout and alignment for their operands, where source and destination operands (i) must reside in the same DRAM subarray (i.e., a group of DRAM rows sharing the same row…
With the strong computation capability, NUMA-based multi-GPU system is a promising candidate to provide sustainable and scalable performance for Virtual Reality. However, the entire multi-GPU system is viewed as a single GPU which ignores…
Data movement in memory-intensive workloads, such as deep learning, incurs energy costs that are over three orders of magnitude higher than the cost of computation. Since these workloads involve frequent data transfers between memory and…
Cache-coherent non-uniform memory access (ccNUMA) systems enable parallel applications to scale-up to thousands of cores and many terabytes of main memory. However, since remote accesses come at an increased cost, extra measures are…
Modern multi-socket architectures offer a single virtual address space, but physically divide main-memory across multiple regions, where each region is attached to a CPU and its cores. While this simplifies the usage, developers must be…
Real-world applications are now processing big-data sets, often bottlenecked by the data movement between the compute units and the main memory. Near-memory computing (NMC), a modern data-centric computational paradigm, can alleviate these…
The increasing prevalence and growing size of data in modern applications have led to high costs for computation in traditional processor-centric computing systems. Moving large volumes of data between memory devices (e.g., DRAM) and…
Non-orthogonal multiple access (NOMA) systems have the potential to deliver higher system throughput, compared to contemporary orthogonal multiple access techniques. For a linearly precoded multiple-input multiple-output (MISO) system, we…
The resurgence of near-memory processing (NMP) with the advent of big data has shifted the computation paradigm from processor-centric to memory-centric computing. To meet the bandwidth and capacity demands of memory-centric computing, 3D…
Energy efficiency and security are two critical issues for mobile edge computing (MEC) networks. With stochastic task arrivals, time-varying dynamic environment, and passive existing attackers, it is very challenging to offload computation…
This article investigates the cache-enabling unmanned aerial vehicle (UAV) cellular networks with massive access capability supported by non-orthogonal multiple access (NOMA). The delivery of a large volume of multimedia contents for ground…
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.…