1,000+ quant interview questions for Jane Street, Citadel, Two Sigma, DE Shaw, and other top quantitative finance firms.
Statistical analysis and quantitative modeling problems
Trading MCQs, probability brainteasers, and market scenarios
Practice quant interview questions on MyntBit - the all-in-one quant learning platform. Free questions available for C++ coding, Python problems, probability brainteasers, and trading MCQs.
Difficulty: Medium
Category: Algorithms & Data Structures
Practice quant interview questions from top firms including Jane Street, Citadel, Two Sigma, DE Shaw, and other leading quantitative finance companies.
Topics: SIMD, vectorization, AVX-256, performance, optimization
You are optimizing a high-frequency trading algorithm that involves computing dot products between a large number of portfolio weights and market data. You have 256 portfolio weights. You are considering using AVX-256 instructions for vectorization, which can process 8 single-precision floating-point numbers (floats) simultaneously. What is the approximate theoretical speedup you can expect over non-vectorized (scalar) code when computing the dot products?
Practice this medium trader interview question on Myntbit - the all-in-one quant learning platform with 1000+ quant interview questions for Jane Street, Citadel, Two Sigma, and other top quantitative finance firms.