# simd Published articles for simd. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Underrust: How does u128 work on a 64-bit processor? DevFeed: [Underrust: How does u128 work on a 64-bit processor?]() Original publisher: [Read original article]() Author: Graham King Published: 2025-04-05T20:45:00Z Content type: tutorial Language: en Sources: [Graham King]() Topics: [cpu](), [x86](), [Rust](), [Compiler](), [Assembly](), [Code]() Tags: [assembly](), [code](), [compiler](), [processors](), [rust](), [simd](), [software](), [sse](), [underrust](), [x86]() ### AI overview The article explains how Rust u128 values work on x86-64 processors whose general-purpose registers are limited to 64 bits. It describes splitting values across two registers and distinguishes this from the use of 128-bit SSE registers for SIMD operations. ### Source excerpt Wherein our hero learns the arcane secrets of the u128 sword in the depths of the Underrust. ## Packing Matrix-Vector Multiplication in Fully Homomorphic Encryption DevFeed: [Packing Matrix-Vector Multiplication in Fully Homomorphic Encryption]() Original publisher: [Read original article]() Published: 2024-09-07T04:18:09Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [homomorphic encryption](), [FHE](), [Encryption](), [layout](), [parallel](), [Python]() Tags: [arithmetic](), [code](), [cryptography](), [data](), [encryption](), [fhe](), [github-repository](), [homomorphic-encryption](), [layout](), [linear-algebra](), [lwe](), [mathematics](), [packing](), [parallel](), [programming](), [python](), [rlwe](), [simd](), [strategies]() ### AI overview This article explains packing for SIMD-style fully homomorphic encryption. It describes how to arrange plaintext data in RLWE ciphertexts so matrix-vector multiplication requires fewer alignment multiplications and rotations, then introduces two basic packing techniques and a computational model. ### Source excerpt In my recent overview of homomorphic encryption, I underemphasized the importance of data layout when working with arithmetic (SIMD-style) homomorphic encryption schemes. In the FHE world, the name given to data layout strategies is called "packing," because it revolves around putting multiple plaintext data into RLWE ciphertexts in carefully-chosen ways that mesh well with the operations you'd like to perform. By "mesh well" I mean it reduces the number of extra multiplications and rotations required merely to align data elements properly, rather than doing the actual computation you care about. ## Converting Between Packings in SIMD-Style FHE DevFeed: [Converting Between Packings in SIMD-Style FHE]() Original publisher: [Read original article]() Published: 2024-09-02T21:01:03Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [FHE](), [homomorphic encryption](), [data](), [Code]() Tags: [compilers](), [encryption](), [fhe](), [github](), [graph-coloring](), [heir](), [homomorphic-encryption](), [mathematics](), [optimization](), [packing](), [permutation](), [programming](), [rlwe](), [simd]() ### AI overview This article explains packing in SIMD-style fully homomorphic encryption and focuses on converting between established packings. It introduces a computational model involving RLWE ciphertext vectors, elementwise operations, cyclic rotations, and differing operation costs. ### Source excerpt In my recent overview of homomorphic encryption, I underemphasized the importance of data layout when working with arithmetic (SIMD-style) homomorphic encryption schemes. In the FHE world, the name given to data layout strategies is called "packing," because it revolves around putting multiple plaintext data into RLWE ciphertexts in carefully-chosen ways that mesh well with the operations you'd like to perform. By "mesh well" I mean it reduces the number of extra multiplications and rotations required merely to align data elements properly, rather than doing the actual computation you care about. ## Underrust: Multiple Return Values DevFeed: [Underrust: Multiple Return Values]() Original publisher: [Read original article]() Author: Graham King Published: 2022-10-18T07:00:00Z Content type: tutorial Language: en Sources: [Graham King]() Topics: [Rust](), [Assembly](), [LLVM](), [Optimization]() Tags: [assembly](), [avx](), [llvm](), [optimization](), [rust](), [simd](), [software](), [underrust]() ### AI overview This article examines how Rust returns values at the ABI and assembly levels. It covers integer and floating-point returns, multiple values, structs, function pointers, caller-stack returns, return-value optimization, and an LLVM SIMD optimization using AVX registers. ### Source excerpt How does Rust return values, and does it make any difference to us programmers? ## Overthinking Leetcode's Two Sum with SIMD DevFeed: [Overthinking Leetcode's Two Sum with SIMD]() Original publisher: [Read original article]() Author: Graham King Published: 2022-10-09T07:00:00Z Content type: tutorial Language: en Sources: [Graham King]() Topics: [Algorithms](), [Code](), [Rust]() Tags: [algorithm](), [algorithms](), [array](), [avx](), [benchmarks](), [code](), [complexity](), [interview](), [performance](), [rust](), [simd](), [software]() ### AI overview This article examines the Two Sum problem by comparing a brute-force linear scan with a map-based solution. It discusses their complexity, implementation costs, and when the linear approach may be faster, including the possibility of using AVX-512 instructions. ### Source excerpt When is the linear scan Two Sum solution faster than a map? What if we use AVX-512 instructions? ## Bitsliced SipHash DevFeed: [Bitsliced SipHash]() Original publisher: [Read original article]() Author: Marek Published: 2013-01-29T23:00:00Z Content type: tutorial Language: en Sources: [Marek Majkowski]() Topics: [Algorithm](), [parallel](), [implementation](), [C](), [Python]() Tags: [algorithm](), [c](), [cpu](), [cycles](), [implementation](), [matrix](), [parallel](), [simd](), [speed](), [sse]() ### AI overview This article explains bitslicing through a SipHash implementation. It describes rewriting an algorithm as simple bit-level logical operations applied to wide SIMD vectors, which can reduce repeated parallel computations. It also discusses the substantial cost of transposing input and output bit matrices. ### Source excerpt Bitsliced SipHash Few days ago I presented a Python and a C implementation of SipHash. This time for no reason whatsoever I implemented a bitsliced version of it. Bitslicing a crypto algorithm is usually done to speed it up when doing massively parallel operations. For example when trying to find a collision with brute force. Bitsliced implementation is only useful if you have a large number of exactly the same mathematical operations to be computed in parallel.