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#Computing

42 posts39 participants8 posts today
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Alex Jimenez

Scientists create ultra-efficient magnetic 'universal memory' that consumes much less energy than previous prototypes

MRAM can be energy-intensive, but a new generation of this technology will enable greater computing power and resilience, as well as much lower energy requirements.

livescience.com/technology/com

Live Science · Scientists create ultra-efficient magnetic 'universal memory' that consumes much less energy than previous prototypesBy Peter Ray Allison
#Computing
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Pustam | पुस्तम | পুস্তম🇳🇵

The Fourier Transform is a mathematical operation that transforms a function of time (or space) into a function of frequency. It decomposes a complex signal into its constituent sinusoidal components, each with a specific frequency, amplitude, and phase. This is particularly useful in many fields, such as signal processing, physics, and engineering, because it allows for analysing the frequency characteristics of signals. The Fourier Transform provides a bridge between the time and frequency domains, enabling the analysis and manipulation of signals in more intuitive and computationally efficient ways. The result of applying a Fourier Transform is often represented as a spectrum, showing how much of each frequency is present in the original signal.

\[\Large\boxed{\boxed{\widehat{f}(\xi) = \int_{-\infty}^{\infty} f(x)\ e^{-i 2\pi \xi x}\,\mathrm dx, \quad \forall\xi \in \mathbb{R}.}}\]

Inverse Fourier Transform:
\[\Large\boxed{\boxed{ f(x) = \int_{-\infty}^{\infty} \widehat f(\xi)\ e^{i 2 \pi \xi x}\,\mathrm d\xi,\quad \forall x \in \mathbb R.}}\]

The equation allows us to listen to mp3s today. Digital Music Couldn’t Exist Without the Fourier Transform: bit.ly/22kbNfi

Gizmodo · Digital Music Couldn't Exist Without the Fourier TransformThis is the Fourier Transform. You can thank it for providing the music you stream every day, squeezing down the images you see on the Internet into tiny
#Fourier#FourierTransform#Transform
Public
Hacker News

Piccolo: Large-Scale Graph Processing with Fine-Grained In-Memory Scatter-Gather

arxiv.org/abs/2503.05116

#HackerNews #Piccolo #Graph #Processing #In-Memory #ScatterGather #LargeScale #Computing

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arXiv.orgPiccolo: Large-Scale Graph Processing with Fine-Grained In-Memory Scatter-GatherGraph processing requires irregular, fine-grained random access patterns incompatible with contemporary off-chip memory architecture, leading to inefficient data access. This inefficiency makes graph processing an extremely memory-bound application. Because of this, existing graph processing accelerators typically employ a graph tiling-based or processing-in-memory (PIM) approach to relieve the memory bottleneck. In the tiling-based approach, a graph is split into chunks that fit within the on-chip cache to maximize data reuse. In the PIM approach, arithmetic units are placed within memory to perform operations such as reduction or atomic addition. However, both approaches have several limitations, especially when implemented on current memory standards (i.e., DDR). Because the access granularity provided by DDR is much larger than that of the graph vertex property data, much of the bandwidth and cache capacity are wasted. PIM is meant to alleviate such issues, but it is difficult to use in conjunction with the tiling-based approach, resulting in a significant disadvantage. Furthermore, placing arithmetic units inside a memory chip is expensive, thereby supporting multiple types of operation is thought to be impractical. To address the above limitations, we present Piccolo, an end-to-end efficient graph processing accelerator with fine-grained in-memory random scatter-gather. Instead of placing expensive arithmetic units in off-chip memory, Piccolo focuses on reducing the off-chip traffic with non-arithmetic function-in-memory of random scatter-gather. To fully benefit from in-memory scatter-gather, Piccolo redesigns the cache and MHA of the accelerator such that it can enjoy both the advantage of tiling and in-memory operations. Piccolo achieves a maximum speedup of 3.28$\times$ and a geometric mean speedup of 1.62$\times$ across various and extensive benchmarks.
Public
Jeffrey Yost

CBI for Computing, Information & Culture is thrilled to announce UPenn History & Soc. of Science ABD Sam Franz is CBI's new Tomash Fellow, for his dissertation project "Calculating Knowledge: Computing, Capitalism, and the Modern University, 1945–1990." #tech #computing #history #sociology

@histodons
@sociology
@commodon

cse.umn.edu/cbi/news/sam-franz

College of Science and EngineeringSam Franz named as the 2025-2026 CBI Tomash FellowMINNEAPOLIS / ST. PAUL (03/21/2025) — We are thrilled to announce that University of Pennsylvania ABD in the History and Sociology of Science Sam Franz is the incoming Erwin and Adelle Tomash Fellow for next academic year. Prior to entering the Penn HSS Doctoral Program, Franz earned his BA with honors from the University of Michigan where he majored in both History and German. He has published in IEEE Annals of the History of Computing, as well as in our own Interfaces: Essays and Reviews in Computing and Culture.Franz has received grants and fellowships from the National Science Foundation, the Association for Computing Machinery, Linda Hall Library, and others. He has presented his work at a host of impressive venues from Harvard University, University of Pennsylvania and University of Michigan to the Society of the History of Technology and the History of Science Society.Sam Franz researches the history of capitalism and computing in the twentieth-century United States. His dissertation project, tentatively titled "Calculating Knowledge: Computing, Capitalism, and the Modern University, 1945–1990," explores knowledge production's increasing centrality in US capitalism by tracing the institutionalization of computing infrastructure and education in US universities. In the second half of the twentieth century, advocates of computing education and infrastructure—including federal officials, academics, university administrators, and corporate managers—saw such technologies as both demanding and serving broader transformations in the US economy. Seemingly local or technical debates about the role of computing on university campuses concealed contentious claims about the emerging postindustrial workplace and enacted them concretely. By analyzing aspirational and real transformations in universities and the workplaces for which their students were destined, Sam's research makes the past and present stakes of the problematic notion of "knowledge economies" tangible. The Tomash Fellowship is possible through the past generous support of CBI’s founders nearly a half century ago, Erwin and Adelle Tomash. A Tomash Fellowship has been awarded each year since the start of the CBI Tomash Fellowship in 1980.Jeffrey R. Yost
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