Futures
Access hundreds of perpetual contracts
TradFi
Gold
One platform for global traditional assets
Options
Hot
Trade European-style vanilla options
Unified Account
Maximize your capital efficiency
Demo Trading
Introduction to Futures Trading
Learn the basics of futures trading
Futures Events
Join events to earn rewards
Demo Trading
Use virtual funds to practice risk-free trading
Launch
CandyDrop
Collect candies to earn airdrops
Launchpool
Quick staking, earn potential new tokens
HODLer Airdrop
Hold GT and get massive airdrops for free
Pre-IPOs
Unlock full access to global stock IPOs
Alpha Points
Trade on-chain assets and earn airdrops
Futures Points
Earn futures points and claim airdrop rewards
Promotions
AI
Gate AI
Your all-in-one conversational AI partner
Gate AI Bot
Use Gate AI directly in your social App
GateClaw
Gate Blue Lobster, ready to go
Gate for AI Agent
AI infrastructure, Gate MCP, Skills, and CLI
Gate Skills Hub
10K+ Skills
From office tasks to trading, the all-in-one skill hub makes AI even more useful.
GateRouter
Smartly choose from 30+ AI models, with 0% extra fees
PyTorch TorchInductor integrates CuteDSL as the automatic tuning backend for matrix multiplication
ME News Report, April 7 (UTC+8), the official PyTorch team recently announced that CuteDSL has been integrated as the fourth matrix multiplication auto-tuning backend into TorchInductor. The selection of this backend is based on three criteria: not adding excessive maintenance burden, not slowing down compilation or benchmarking times, and providing better performance on target workloads. CuteDSL, actively developed by NVIDIA, offers optimized kernel templates, with compilation times comparable to existing backends and significantly better than the CUTLASS C++ path that requires full nvcc compilation. This backend is built on the same abstraction as CUTLASS C++, written in Python, with faster compilation and easier maintenance, and has demonstrated strong performance in FP8 GEMM and Epilogue fusion. The team focuses on optimizing GEMM (matrix multiplication) because it accounts for the main computational cost in Transformer models. CuteDSL generates low-level code by providing handcrafted optimized templates, avoiding the complexity of writing kernels from scratch, and fully exposing thread and memory hierarchy, supporting architecture-specific features. (Source: InFoQ)