arXiv subjects

Shoaib Akram

Publications and source records attributed to Shoaib Akram.

3 recordsLinked to original sources

arXiv2025

Impact of Annealing Temperature on the Energy Storage Performance of CoO2 Nanoparticles Synthesized via Solid State Reaction

The solid-state reaction was used to synthesize CoO2 nanostructured material. Cobalt nitrate tetrahydrate and sodium oxide (NaOH) were combined to produce CoO2 nanostructured material. The three synthesized working electrodes were each tested individually, using 3 M KOH as the electrolyte. The CV analysis of a three-electrode system revealed redox peaks, indicating Faradaic processes. The estimated specific capacitances of CoO2, CoO2 (250oC), CoO2 (300oC) nanostructured material at scan rates of (10) mVs-1 is (223, 348, and 473) Fg-1. The diffraction peaks at 2{\theta} = 26.264o, 33.527o, 37.579, 51.264 and 54.367o correspond respectively to the diffraction planes of 111, 112, 200, 211, and 311 of CoO2 nanostructured material. The annealing temperature, which affects the bandgap, can influence the size, shape, and crystallinity of nanostructures. For the unannealed material, the energy bandgap of CoO2 is 2.00 eV, whereas for the annealed material it ranges from 1.77 to 1.86 eV.

cond-mat.mtrl-sci↗

arXiv2023

Garbage Collection or Serialization? Between a Rock and a Hard Place!

Big data analytics frameworks, such as Spark and Giraph, need to process and cache massive amounts of data that do not always fit on the heap. Therefore, frameworks temporarily move long-lived objects outside the managed heap (off-heap) on a fast storage device. Unfortunately, this practice results in: (1) high serialization/deserialization (S/D) cost, and (2) high memory pressure when off-heap objects are moved back to the managed heap for processing. In this paper, we propose TeraHeap, a system that eliminates S/D overhead and expensive GC scans for a large portion of the objects in big data frameworks. TeraHeap relies on three concepts. (1) It eliminates S/D cost by extending the managed runtime (JVM) to use a second high-capacity heap (H2) over a fast storage device. (2) It reduces GC cost by fencing the garbage collector from scanning H2 objects. (3) It offers a simple hint-based interface, which allows frameworks to leverage knowledge about objects for populating H2. We implement TeraHeap in OpenJDK and evaluate it with 15 widely used applications in two real-world big data frameworks, Spark and Giraph. Our evaluation shows that for the same DRAM size, TeraHeap improves performance by up to 73% and 28% compared to native Spark and Giraph, respectively. Also, it provides better performance by consuming up to 8x and 1.2x less DRAM capacity than native Spark and Giraph, respectively. Finally, it outperforms Panthera, a garbage collector for hybrid memories, by up to 69%.

cs.PL↗

arXiv2018

Emulating Hybrid Memory on NUMA Hardware

Non-volatile memory (NVM) has the potential to disrupt the boundary between memory and storage, including the abstractions that manage this boundary. Researchers comparing the speed, durability, and abstractions of hybrid systems with DRAM, NVM, and disk to traditional systems typically use simulation, which makes it easy to evaluate different hardware technologies and parameters. Unfortunately, simulation is extremely slow, limiting the number of applications and dataset sizes in the evaluation. Simulation typically precludes realistic multiprogram workloads and considering runtime and operating system design alternatives. Good methodology embraces a variety of techniques for validation, expanding the experimental scope, and uncovering new insights. This paper introduces an emulation platform for hybrid memory that uses commodity NUMA servers. Emulation complements simulation well, offering speed and accuracy for realistic workloads, and richer software experimentation. We use a thread-local socket to emulate DRAM and the remote socket to emulate NVM. We use standard C library routines to allocate heap memory in the DRAM or NVM socket for use with explicit memory management or garbage collection. We evaluate the emulator using various configurations of write-rationing garbage collectors that improve NVM lifetimes by limiting writes to NVM, and use 15 applications from three benchmark suites with various datasets and workload configurations. We show emulation enhances simulation results. The two systems confirm most trends, such as NVM write and read rates of different software configurations, increasing our confidence for predicting future system effects. Emulation adds novel insights, such as the non-linear effects of multi-program workloads on write rates.

cs.PL↗