Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2003.01216

High Performance Parallel Sort for Shared and Distributed Memory MIMD

Thoria Alghamdi·Gita Alaghband
View original source ↗Read source document ↗Export metadata ↓

Abstract

We present four high performance hybrid sorting methods developed for various parallel platforms: shared memory multiprocessors, distributed multiprocessors, and clusters taking advantage of existence of both shared and distributed memory. Merge sort, known for its stability, is used to design several of our algorithms. We improve its parallel performance by combining it with Quicksort. We present two models designed for shared memory MIMD (OpenMP): (a) a non-recursive Merge sort and (b) a hybrid Quicksort and Merge sort. The third model presented is designed for distributed memory MIMD (MPI) using a hybrid Quicksort and Merge sort. Our fourth model is designed to take advantage of the shared memory within individual nodes of todays cluster systems, and to eliminate all internal data transfers between different nodes, Our model implements a one-step MSD-Radix to distribute data in ten packets (MPI) while parallel cores of each node use Quicksort to sort their data partitions sequentially then merge and sort them in parallel employing the OpenMp. The performances of all developed models outperform the baseline performance. Hybrid Quicksort and Merge sort outperformed Hybrid Memory Parallel Merge Sort using Hybrid MSD-Radix and Quicksort in Cluster Platforms when sorting small size data, but with larger data the speedup of Hybrid Memory Parallel Merge Sort Using Hybrid MSD-Radix and Quicksort in Cluster Platforms becomes bigger and it keeps improving. The speedup of Distributed Memory Parallel Hybrid Quicksort and Merge Sort is the best.

Explore related subjects

cs.DC

Keep this discovery

BibTeXRIS

Thoria Alghamdi, Gita Alaghband. 2020-03-02. High Performance Parallel Sort for Shared and Distributed Memory MIMD. https://arxiv.org/abs/2003.01216

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Online Treasure Hunt in Vertex-Permuted Dynamic Rings

We study the problem of treasure hunt by a group of $k \geq 1$ agents in vertex-permuted dynamic rings (VP). In this model, the $n$ vertices remain on a ring but are permuted at each time step. We first show that treasure hunt is impossible for any $k \leq n-3$ agents, if there are no restrictions on the sequence of permutations used in the dynamic ring. We then study the $VP(\delta)$ setting, in which for every pair $i, j$ of vertices, the edge $(i, j)$ is guaranteed to appear within $\delta$ steps. We show that the class $VP(\delta)$ is feasible only for $\delta \geq \left\lceil \frac{n-1}{2}\right\rceil$. For the one-agent case, we show a tight bound of $\Theta(\delta n)$ on the worst-case search time as well as competitive ratio of any online algorithm for treasure hunt, provided $\delta \geq 2n$. We then give an optimal algorithm for $k$ agents, thereby showing that $k$ agents can obtain a speedup of $k$ on the worst-case search time. Finally, in the R-VP setting, in which in every step, the vertices are arranged as a ring according to a random permutation, we show that treasure hunt takes expected $\Theta(n)$ steps against an oblivious adversary and $\Theta(n \log n)$ steps against an adaptive adversary.

cs.DC↗
arXiv2026

The Computing Channel: How Modulation Programs the Airwaves

Distributed computing and distributed artificial intelligence require frequent exchanges of intermediate results, although many applications need only an aggregate rather than messages from individual devices. Conventional systems recover each message before computing the aggregate, whereas over-the-air computation (OAC) exploits simultaneous transmission to obtain it directly. However, dominant OAC implementations rely on analog signaling, creating a mismatch with finite-precision data and digital communication procedures. This article presents digital function-oriented communication, in which finite-alphabet symbol representations and receiver decisions are jointly designed so that multiple-access superposition encodes the desired function without recovering individual inputs. We introduce its computational-constellation principle, main design approaches, extensions, and implementation challenges. Federated edge learning illustrates how the framework can reduce user-dependent data-bearing resources while operating directly on quantized model updates.

cs.DC↗
arXiv2026

Can AI Remediate Backend Failures Safely? GuardedAct with Blast-Radius-Aware Sandboxing

Large Language Models (LLMs) have shown promising capabilities in generating remediation actions for microservice failures. However, directly executing AI-generated repair actions in production risks cascading collateral damage. We propose GuardedAct, a sandbox-first remediation framework that interposes a blast-radius-aware verification layer between the LLM action generator and the production environment. GuardedAct operates in four phases: (1) ingesting a diagnosis report together with the live system topology and recent telemetry, (2) prompting an LLM to produce a ranked list of candidate remediation actions, (3) simulating each action in a lightweight digital-twin sandbox that estimates the blast radius and assigns a risk label, and (4) enforcing a rollback-confidence gate that auto-executes only low-risk actions while escalating high-risk ones for human review. We evaluate GuardedAct on five fault scenarios injected into the DeathStarBench social-network application. Experimental results show that GuardedAct achieves an overall recovery rate of 87.4% while reducing collateral damage by 79.7% relative to direct LLM execution (from 25.6% to 5.2%), at the cost of a modest sandbox-induced increase in mean time to recovery (approximately 8 s). Ablation studies confirm that each component contributes meaningfully to the safety-speed trade-off.

cs.DC↗