Concise monthly summary for March 2026 focusing on performance optimizations and stability improvements across GPU-accelerated ML workloads in modular/modular and modularml/mojo. Key accelerations include CUDA graph capture reduction for DeepSeek V3/R1 on B200, SnapMLA decoding optimizations with per-token scaling and BF16 TMA tiles, and KV scale loading optimization for SnapMLA on GPU, plus a NaN-related bug fix in the SnapMLA kernel. These changes improved runtime performance, reduced CUDA graph overhead, lowered memory access latency, and increased overall reliability of GPU-accelerated ML workloads.

February 2026 — Modular/Modular Monthly Summary Key features delivered: - FP8 support for MLA decoding and QKV FP8 operations with memory/performance benchmarks to guide usage. - Concurrent N-stage separation in the MMA PV pipeline to allow first-part corrections while the second MMA runs, increasing throughput. - Split-K MLA decoding improvements enabling multiple splits, partial outputs handling, and improved numerical stability with variable KV caches. - Kernel optimization: Combine kernel redesigned to maximize GPU throughput, including handling of empty work and new batch/cache length configurations. - Blockwise scaling for MLA decode on Sm100 to support variable quantization granularity and optimize scale-value memory usage. Major bugs fixed: - MLA decode out-of-bounds (OOB) fix on flash MLA decode to improve robustness. - Fix OOB when cache_length is not accurate, ensuring correct last-page handling. - Correct MLA Decode Sm100 results for Variable Sequence Length (Q) during decoding. - Fix matmul kernel launch ordering issue at PDL level 1 to avoid incorrect synchronization. - CI/test reliability improvements by removing flaky tests and reducing timeouts in GPU kernel tests. Overall impact and accomplishments: The month delivered substantial efficiency and robustness gains across the MLA/QKV decode path and the GPU execution pipeline. FP8 support and the Split-K and N-stage separation improvements enable higher throughput and better memory utilization for long-context decoding. Kernel optimizations and blockwise scaling reduce latency and increase throughput on Sm100/modern GPUs, while robustness fixes and CI improvements reduce runtime risk and accelerate validation. Collectively, these changes enable scalable, reliable deployment of high-throughput MLA/QKV workloads with improved performance and predictability. Technologies/skills demonstrated: - GPU kernel development and optimization (CUDA), including MVLA/MMA PV and MLA decode paths - Quantization-aware computing (FP8) and KV-cache management - Algorithmic enhancements (Split-K, concurrent N-stage, blockwise scaling) - Performance benchmarking, profiling, and memory footprint optimization - CI reliability improvements and robust software quality practices

January 2026 (2026-01) — Modular/modular delivered feature-driven performance improvements for MLA decoding and memory throughput. Key changes include enabling sequence lengths greater than one for MLA decoding, removing the 64x64 tile size restriction for TMA Load and MMA operations, and updating the testing framework for comprehensive coverage. No major bugs fixed during this period; outcomes improve ML workload scalability and GPU utilization, with broader test coverage to reduce regression risk.

December 2025 monthly summary for modular/modular focused on delivering high-impact ML capabilities in the SM100 path. Delivered Multi-head Latent Attention (MLA) decoding support for SM100 with kernel-level enhancements, memory management optimizations, and GPU synchronization improvements to boost attention throughput and efficiency.

November 2025: Delivered GPU-accelerated FA4 implementation using warp specialization with the Cutlass library to accelerate matrix operations in the modular/modular project. This work focused on deep-learning workflows, improving performance of pipelines and integration tests by tuning tolerance and distance thresholds for better accuracy. Commits capturing the work include 0e9587dc96d2462e0edccfa8135a5a0051cfbd6a and 0a8a36669e2f205b32eb5654af206912cfee19a8, aligned with the MODULAR_ORIG_COMMIT_REV_ID and MAX_INTEGRATION_TESTS_REV_ID references. Major bugs fixed: none reported this month. Impact: faster deep-learning workloads on GPUs, more reliable integration tests, and a stronger foundation for scalable GPU acceleration in modular/modular. Technologies/skills demonstrated: GPU programming with Cutlass, warp specialization strategies, CUDA-based optimization, performance tuning, and test-driven integration.

2025-10 monthly summary focused on performance and reliability: Implemented fast exponential approximations in stdlib (exp2/exp) using a cubic FA-4 Horner polynomial with scalar and SIMD paths and GPU tests; added a LoRA-oriented kernel for grouped QKV permutation (lora_shrink_qkv_permute_3mn_sm100) featuring storage reuse and an epilogue for planar outputs, plus comprehensive tests and documentation; fixed NVPTX denormalized FP handling for sm_90+ with sign preservation for f16/f32 and updated PTX tests for optional ftz modifiers. These efforts deliver faster math operations, robust GPU compatibility, and ML-oriented kernel support, driving performance gains in numerical workloads and overall platform reliability.