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DeepSeek recently open-sourced six powerful software libraries to tackle problems in LLM teaining, inference and data infrastructure. At the heart of its open-source DeepEP library lies a robust PTX customization kernel, designed to maximize efficiency for deep compute-intensive tasks. This blog post explores the intricacies of DeepSeek's PTX kernel and its role in the DeepEP framework.

What is PTX and Why Does It Matter?

PTX, or Parallel Thread Execution, is an intermediate assembly language used by NVIDIA GPUs to execute programs written in CUDA. By directly interacting with PTX, developers gain fine-grained control over GPU operations, enabling them to bypass some of the inefficiencies introduced by higher-level abstractions like standard CUDA kernels While working at this level requires expertise, it opens up opportunities for groundbreaking optimizations—something DeepSeek has capitalized on extensively.

The Role of `utils.cuh` in DeepEP

This file contains critical implementations that enable the integration of custom PTX instructions into CUDA workflows. For instance:

Custom PTX instructions : Within utils.cuh, you'll find code snippets where DeepSeek overrides default CUDA compiler decisions with hand-tuned PTX logic. These modifications target specific hardware features, ensuring optimal utilization of resources.
Memory Transfer Optimization : One of the standout achievements in DeepEP is the reduction of unnecessary memory transfers between host and device. By embedding PTX instructions directly, DeepSeek minimizes latency and boosts overall efficiency
Enhanced Memory Access :
1. Relaxed Memory access: Instructions like ld.relaxed and st.relaxed minimize synchronization overhead by avoiding unnecessary memory barriers.
2. Non-Allocating Loads: The ld.global.nc.L1::no_allocate instruction bypasses L1 cache allocation, reducing cache pollution for certain workloads.
3. Volatile Loads: Functions like ld.volatile.global ensure data consistency by bypassing compiler optimizations.
Atomic Operations : Atomic instructions are crucial for ensuring data integrity in parallel environments. DeepEP includes custom atomic functions like atom.add.release.sys.global, which combine atomicity with relaxed or release semantics for fine-grained control over memory ordering.

How Does It Work?

The customization kernel relies heavily on inline assembly within CUDA, allowing developers to inject raw PTX instructions directly into their code. For example:

__device__ __forceinline__ void st_na_relaxed(const int *ptr, int val) {
    asm volatile("st.relaxed.gpu.global.L1::no_allocate.b32 [%0], %1;" : : "l"(ptr), "r"(val));
}

This function performs a relaxed store operation, bypassing the L1 cache for improved performance in specific scenarios.

Additionally, macros like UNROLLED_WARP_COPY enable optimized data movement within warps by unrolling loops and minimizing instruction latency:

#define UNROLLED_WARP_COPY(UNROLL_FACTOR, LANE_ID, N, DST, SRC, LD_FUNC, ST_FUNC) \
{ \
    constexpr int kLoopStride = 32 * (UNROLL_FACTOR); \
    typename std::remove_reference<decltype(LD_FUNC((SRC) + 0))>::type unrolled_values[(UNROLL_FACTOR)]; \
    auto __src = (SRC); \
    auto __dst = (DST); \
    for (int __i = (LANE_ID); __i < ((N) / kLoopStride) * kLoopStride; __i += kLoopStride) { \
        _Pragma("unroll") \
        for (int __j = 0; __j < (UNROLL_FACTOR); ++__j) \
            unrolled_values[__j] = LD_FUNC(__src + __i + __j * 32); \
        _Pragma("unroll") \
        for (int __j = 0; __j < (UNROLL_FACTOR); ++__j) \
            ST_FUNC(__dst + __i + __j * 32, unrolled_values[__j]); \
    } \
}

This macro is particularly useful for optimizing memory-bound operations in deep learning models.

What is PTX and Why Does It Matter?

The Role of utils.cuh in DeepEP

How Does It Work?

The Role of `utils.cuh` in DeepEP