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All forum topics; Node JS Agent download not working for 32 bits architecture inside Windows CommunityUser. Splunk Employee 0 Karma Reply. All forum topics; Query");console.log(results);Example of a binary hamming distance query over BigUint64Array (64 bits processed at a time using pure JS):(Insanely Fast) Query Binary Embeddings Using Hamming Distance Over WebAssembly SIMD (+128 bits at a time, shorter CPU cicles)The WebAssembly SIMD implementation processes 128 bits per iteration (via v128.xor) compared to 64 bits per iteration in the JavaScript implementation using BigUint64Array. This alone gives a theoretical 2x speedup.However SIMD instructions execute XOR, popcount, and similar operations on multiple data lanes in parallel. This reduces the number of CPU cycles required for the same amount of work compared to sequential bitwise operations in JavaScript. SIMD in WebAssembly is likely 2x to 4x faster or more over big vectors.Check haming_distance_simd.wat for the WASM source code. Compiled using wat2wasm.const results = await db.queryBinarySIMD( "Find similar texts based on this query");console.log(results);Example of a binary hamming distance query over WebAssembly SIMD (+128 bits at a time, shorter CPU cicles):The logs show offsets (0, 16, 32), which means the code processes 128 bits (16 bytes) at a time. Since the total vector is 384 bits, it takes 3 steps (384 ÷ 128 = 3), confirming 128-bit SIMD processing.For NextjsIf you're using Next.js, you may need to configure Webpack to work with Transformers.js. Add the following to your next.config.js file: { // Override the default webpack configuration config.resolve.alias = { ...config.resolve.alias, "onnxruntime-node$": false, // Disable onnxruntime-node for browser environments "sharp$": false, // optional - Disable sharp package (used by some image processing packages) }; return config; },"> webpack: (config) => { // Override the default webpack configuration config.resolve.alias = { ...config.resolve.alias, "onnxruntime-node$": false, // Disable onnxruntime-node for browser environments "sharp$": false, // optional - Disable sharp package (used by some image processing packages) }; return config; },ContributingFeel free to fork the repository, create issues, and submit pull requests. We welcomeComments
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2025-04-06Query");console.log(results);Example of a binary hamming distance query over BigUint64Array (64 bits processed at a time using pure JS):(Insanely Fast) Query Binary Embeddings Using Hamming Distance Over WebAssembly SIMD (+128 bits at a time, shorter CPU cicles)The WebAssembly SIMD implementation processes 128 bits per iteration (via v128.xor) compared to 64 bits per iteration in the JavaScript implementation using BigUint64Array. This alone gives a theoretical 2x speedup.However SIMD instructions execute XOR, popcount, and similar operations on multiple data lanes in parallel. This reduces the number of CPU cycles required for the same amount of work compared to sequential bitwise operations in JavaScript. SIMD in WebAssembly is likely 2x to 4x faster or more over big vectors.Check haming_distance_simd.wat for the WASM source code. Compiled using wat2wasm.const results = await db.queryBinarySIMD( "Find similar texts based on this query");console.log(results);Example of a binary hamming distance query over WebAssembly SIMD (+128 bits at a time, shorter CPU cicles):The logs show offsets (0, 16, 32), which means the code processes 128 bits (16 bytes) at a time. Since the total vector is 384 bits, it takes 3 steps (384 ÷ 128 = 3), confirming 128-bit SIMD processing.For NextjsIf you're using Next.js, you may need to configure Webpack to work with Transformers.js. Add the following to your next.config.js file: { // Override the default webpack configuration config.resolve.alias = { ...config.resolve.alias, "onnxruntime-node$": false, // Disable onnxruntime-node for browser environments "sharp$": false, // optional - Disable sharp package (used by some image processing packages) }; return config; },"> webpack: (config) => { // Override the default webpack configuration config.resolve.alias = { ...config.resolve.alias, "onnxruntime-node$": false, // Disable onnxruntime-node for browser environments "sharp$": false, // optional - Disable sharp package (used by some image processing packages) }; return config; },ContributingFeel free to fork the repository, create issues, and submit pull requests. We welcome
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2025-04-1206 2:forl = 7 to 0 do3:for m = 3 to 0 do4: if the l-th bit of R 16 + m ==1 then5: R 0 ← R 0 + R 25 6:for k = 0 to 3 do7: R 8 + m + k ← R 8 + m + k ⨁ R 20 + k 8:end for9:else10:for k = 0 to 3 do11: R 24 ← R 24 ⨁ R 20 + k 12:end for13:end if14:end for15: ( R 15 , … , R 8 ) ← ( R 15 , … , R 8 ) ≪ 1 16:end for…In addition, the performance is improved further by applying Liu et al’s multiplication method to the proposed method. Seo and Kim proposed to shift 40-bit multiplicand (A) for a 64-bit multiplier, rather than shifting 64-bit accumulator. This approach reduces 29 shift instructions per 32-bit multiplication operation. However, the multiplication method suggested by Seo and Kim does not show a big difference in performance compared to the method suggested by Liu et al. According to Seo and Kim, the number of shift instructions can be reduced compare to Liu et al’s method. However, the XOR instruction goes one more operation per bit, which leads to addition of 32 more XOR instructions when calculation 40-bit multiplicand (A). Since five registers are needed to store the 40-bit multiplicand (A), one more register is required compared to the Liu et al’s technique. Liu et al.’s approach is used for multiplication and one register is saved. These spare registers are used in the Karatsuba algorithm to improve the performance. For the optimal number of register utilization, the version without using spare registers is also investigated. Currently, RISC-V introduces new architecture for future microcontrollers. The optimal register utilization can contribute to the optimal architecture design. 4.2. Karatsuba Algorithm for GHASHKaratsuba algorithm is well known asymptotically fast multiplication method and the proposed implementation also utilizes the Karatsuba algorithm for high performance.First, the multiplication is performed with lower 32-bit of 64-bit operands ( A [ 3 ∼ 0 ] , B [ 3 ∼ 0 ] )
2025-04-23